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Arcminute Microkelvin Imager

The Arcminute Microkelvin Imager (AMI) consists of a pair of interferometric radio telescopes - the Small and Large Arrays - located at the Mullard Radio Astronomy Observatory near Cambridge. AMI was designed, built and is operated by the Cavendish Astrophysics Group. AMI was designed, primarily, for the study of galaxy clusters by observing secondary anisotropies in the cosmic microwave background (CMB) arising from the Sunyaev–Zel'dovich (SZ) effect. Both arrays are used to observe radiation with frequencies between 12 and 18 GHz, and have very similar system designs. The telescopes are used to observe both previously known galaxy clusters, in an attempt to determine, for example, their masses and temperatures, and to carry out surveys, in order to locate previously undiscovered clusters.

AMI Large Array
The AMI Large Array (AMI LA) is composed of eight 12.8-metre-diameter, equatorially mounted parabolic antennas, which were previously part of the Ryle Telescope. The antennas are separated by distances ranging between 18 and 110 m. The telescope has an angular resolution of approximately 30 arcseconds. The LA is used to image the radio sources (mainly radio galaxies) that contaminate the Small Array observations of the CMB. The LA is being used to carry out the Tenth Cambridge Survey of radio sources. The first results from the survey were used to extend the measured 15-GHz source counts to sub-millijansky levels; this is an order of magnitude deeper than achieved by the Ninth Cambridge Survey, which was the first survey of significant sky coverage at a comparable radio frequency.

AMI Small Array
The AMI Small Array (AMI SA) consists of 10 3.7-m-diameter antennas, similar in design to those of the LA. They are arranged at intervals of between 5 and 20 m. The SA has an angular resolution of approximately 3 arcminutes and is used to image, at high resolution, the galaxy clusters of interest. The SA is being used to search for previously unknown galaxy clusters; results from the first such cluster, detected using AMI, were released in December 2010. In addition to observations of galaxy clusters, the SA has been used to carry out observations, amongst other things, towards supernova remnants and anomalous microwave emission.

title: Arcminute Microkelvin Imager


text: User talk:205.155.32.39

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: User talk:205.155.32.39


text:
Fitzhugh Lee

Fitzhugh Lee (November 19, 1835 – April 28, 1905) was a Confederate cavalry general in the American Civil War, the 40th Governor of Virginia, diplomat, and United States Army general in the Spanish–American War. He was the son of Sydney Smith Lee, a captain in the Confederate States Navy, and the nephew of Robert E. Lee.

Early life and education
Lee was born on November 19, 1835, at Clermont in Fairfax County, Virginia. He was the grandson of "Light Horse Harry" Lee, a nephew of Robert E. Lee and Samuel Cooper, and cousin of George Washington Custis Lee, W.H.F. "Rooney" Lee, and Robert E. Lee, Jr. His father, Sydney Smith Lee, served under Commodore Perry in Japanese waters and rose to the rank of Captain; his mother, Anna Maria Mason Lee, was a granddaughter of George Mason and the sister of James Murray Mason.

Lee attended the United States Military Academy at West Point, New York, where he graduated in 1856, and was commissioned a second lieutenant in the 2nd Cavalry Regiment (later redesignated the 5th Cavalry Regiment), which was commanded by Colonel Albert Sidney Johnston, and in which his uncle, Robert E. Lee, was lieutenant colonel. As a cavalry subaltern, he distinguished himself by his gallant conduct in actions against the Comanches in Texas and was severely wounded in a fight in Nescutunga, Texas, in May 1859. In May 1860, he was appointed instructor of cavalry tactics at the U.S. Military Academy, but resigned his commission after Virginia declared secession and alliance with the Confederacy at the beginning of the American Civil War.

American Civil War
Fitzhugh Lee joined the Confederate States Army as a lieutenant of cavalry and served at first as a staff officer to Brig. Gen. Richard Stoddert Ewell at the First Battle of Manassas. He was promoted to lieutenant colonel of the 1st Virginia Cavalry in September 1861, serving under Colonel "Grumble" Jones. Lee replaced Jones in March 1862, after the elections held in the regiment in accordance with the Confederate Conscripion Act of April 16, 1862. He was promoted to brigadier general on July 24, 1862.

During the Northern Virginia Campaign, Fitzhugh Lee received notoriety by arriving late for a cavalry concentration, allowing U.S. Army cavalry to raid Stuart's headquarters and capture his famous plumed hat and cape. However, during the subsequent Confederate raid on Catlett's Station, he captured the headquarters tent and dress uniform of U.S. Maj. Gen. John Pope. Fitzhugh Lee gave Pope's coat to Stuart as compensation for the hat he had lost.

Fitzhugh Lee performed well in the Maryland Campaign of 1862, covering the Confederate infantry's withdrawal from South Mountain, delaying the U.S. Army advance to Sharpsburg, Maryland, before the Battle of Sharpsburg around Antietam Creek, and covering his army's recrossing of the Potomac River into Virginia. Stuart's cavalry made its second ride around the U.S. Army in the Chambersburg Raid before returning in time to screen Robert E. Lee's movement towards Fredericksburg, where the cavalry defended the extreme right of the Confederate line. Fitzhugh Lee conducted the cavalry action of Kelly's Ford (March 17, 1863) with skill and success, where his 400 troopers captured 150 men and horses with a loss of only 14 men. In the Battle of Chancellorsville in May 1863, Lee's reconnaissance found that the U.S. Army's right flank was "in the air", which allowed the successful flanking attack by Maj. Gen. Thomas Jonathan "Stonewall" Jackson, a movement led by Fitzhugh Lee's cavalry.

After Chancellorsville, Lee was incapacitated by inflammatory rheumatism, missing a month of action, which included the significant cavalry operations at the Battle of Brandy Station. He recovered in time to lead a brigade in Jeb Stuart's third ride around the U.S. Army, in the early days of the Gettysburg Campaign, with his most significant contribution being at the Battle of Carlisle. During the Battle of Gettysburg, his brigade fought unsuccessfully at East Cavalry Field. Stuart's report singled out no officer in his command for praise except Fitz Lee, who he said was "one of the finest cavalry leaders on the continent, and richly [entitled] to promotion." During the withdrawal from Gettysburg, Lee's brigade held the fords at Shepherdstown to prevent the U.S. Army from following across the Potomac River. Lee was promoted to major general on August 3, 1863, and continued to serve under Maj. Gen. Stuart's command, despite Stuart not receiving a promotion following his questionable conduct in the Gettysburg Campaign. While his uncle maneuvered the Army of Northern Virginia back into central Virginia, Lee's division launched a successful ambush on U.S. Army cavalry at the Battle of Buckland Mills that fall.

In the Overland Campaign the following spring, Lee was constantly employed as a divisional commander under Stuart. Following the Battle of the Wilderness, Lee's cavalry division played a pivotal role in impeding the U.S. Army in its race to the Battle of Spotsylvania Court House. While fighting at Spotsylvania, Gen. Stuart was detached from the army to thwart U.S. cavalry commander Phillip Sheridan's raid on Richmond. Stuart took Fitzhugh Lee's division with him. The mission ultimately ended in the mortal wounding of Gen. Stuart at the Battle of Yellow Tavern and Lee's inability to break through the United States Colored Troops' defense of Fort Pocahontas in Charles City County and Fort Powhatan in Prince George County. After Stuart's death, Lee served under Maj. Gen. Wade Hampton. Hampton, who had been Lee's peer for much of the war, was promoted to replace Stuart due to his seniority and more significant experience; some observers at the time had cynically expected Robert E. Lee's nephew to receive the command.

At the Battle of Trevilian Station, Hampton's cavalry prevented Gen. Sheridan's cavalry from aiding General David Hunter's force in western Virginia, where it was sure to have inflicted significant damage on General Robert E. Lee's supply and communication lines. The battle also served to screen Lt. Gen. Jubal A. Early's move from Richmond to aid Lynchburg, which Hunter was set to besiege. Hampton's cavalry corps shadowed Sheridan's return to Petersburg.

Fitzhugh Lee subsequently joined Early for his campaign against Maj. Gen. Philip Sheridan in the Shenandoah Valley, and at Third Winchester (September 19, 1864) three horses were shot under him, and he was severely wounded. When General Hampton was sent to assist General Joseph E. Johnston in North Carolina, the command of the whole of Robert E. Lee's cavalry devolved upon Fitzhugh Lee on March 29, 1865, but the surrender at Appomattox followed quickly upon the opening of the campaign. Fitzhugh Lee himself led the last charge of the Confederates on April 9 that year at Farmville, Virginia.

Post-Civil War
After the end of the Civil War, Fitzhugh Lee devoted himself to farming in Stafford County, Virginia, and was conspicuous in his efforts to reconcile the Southern people to the issue of the war, which he regarded as a final settlement of the questions at issue. In 1875, he attended the Battle of Bunker Hill centennial at Boston and delivered an address. In 1885, he was a member of the board of visitors of West Point, and from 1886 to 1890 was governor of Virginia having defeated in 1885 Republican John Sergeant Wise with 52.77% of the vote.

Lee commanded the third division at President Grover Cleveland's inaugural parades in 1885 and 1893.

Consul-general in Havana
In April 1896, Lee was appointed consul-general at Havana by President Cleveland, with duties of a diplomatic and military character added to the usual consular business. In this post, in which he was retained by President William McKinley until 1898, he was the first called upon to deal with a situation of great difficulty, which culminated with the destruction of the warship USS Maine (ACR-1). When war was declared between Spain and the United States, he re-entered the United States Army.

He was one of four ex-Confederate Army general officers who were made major generals of United States Volunteers; the others being Matthew Butler, Joseph Wheeler, and Thomas L. Rosser. Fitzhugh Lee commanded the 7th Army Corps but took no part in the actual operations in Cuba. He was military governor of Havana and Pinar del Río in 1899, subsequently commanded the Department of the Missouri, and retired in 1901 as a brigadier general, U.S. Army.

Lee was an early leader of the committee for the Jamestown Exposition, which was held after his death at Sewell's Point on Hampton Roads in 1907.

Lee wrote the article about Robert E. Lee in the Great Commanders series (1894), General Lee, a wartime biography (1894), and Cuba's Struggle Against Spain (1899).

Death
Lee died in Washington, D.C., and is buried in Hollywood Cemetery, Richmond.

Legacy
He was indicted for treason but the indictment was withdrawn in February 1869. Lee then received a pardon. The Seventh Army Corps Association and Auxiliary dedicated a monument to Lee's service to the 7th Army Corps from 1898 to 1899, placing it in Monroe Park, Richmond, Virginia; the City of Richmond removed the monument on July 9, 2020, in recognition of Lee's participation in the Confederacy.

title: Fitzhugh Lee


text:
The Embargo

The Embargo is a historical poem written by the American poet William Cullen Bryant in 1808. Bryant was a critic of Jeffersonian political philosophy, and the work was his attempt to satirize a shipping embargo imposed by Thomas Jefferson at the time.

title: The Embargo


text:
John D. Imboden

John Daniel Imboden (February 16, 1823 – August 15, 1895), American lawyer, Virginia state legislator, and a Confederate army general. During the American Civil War, he commanded an irregular cavalry force. After the war, he resumed practicing law, became a writer, and was active in land development founding the town of Damascus, Virginia.

Early and family life
Imboden was born near Staunton, Virginia, in the Shenandoah Valley to farmer George William Imboden and Isabella Wunderlich who bore eleven children. His father fought in the War of 1812. Imboden received a private education locally at the Staunton Academy, then in 1841–1842 he attended Washington College. By 1850, John and his brother Frank no longer lived on the family farm, which his father worked with the help of his younger brothers George, James, Jacob, and a white hired hand named Robert Walkey; his sisters Eliza and Frances helped their mother at the home.

Early career
He took a job teaching at the Virginia School for the Deaf and the Blind in Staunton. Imboden also read law, was admitted to the Virginia bar, and entered into partnership with William Frazier to create a law firm. In December 1844, Imboden became a member of the Staunton Masonic lodge, Number 13, Ancient Free and Accepted Masons (A.F. & A.M.) In the 1850 Federal census, Imboden owned four slaves, a 16-year-old mulatto male, a 23-year-old black female and 12- and three-year-old girls. His slaveholdings increased to 7 in 1860, the mulatto male becoming 26 years old, and working alongside two 20-year-old black women, a 24-year-old black woman and black boys aged 6 and 14 as well as a 14-year-old black girl.

Augusta County voters elected Imboden as one of their two (part-time) delegates in the House of Delegates in 1850, alongside veteran John Marshall McCue. As a result of greater representation accorded western Virginia counties and the 1850 census, Augusta county was allocated a third delegate in 1852, so as the third-highest vote-getter that year, Imboden was re-elected, this time serving alongside James Walker and John A. Tate. However, Augusta County voters selected three other men in 1853, although Imboden again was the third highest vote-getter and returned to the legislature in 1855, this time alongside Boliver Christian and Adam McChesney. Imboden again failed to win re-election in 1857. In response to John Brown's Raid at Harper's Ferry, Imboden helped found the Staunton Light Artillery, partially funding it as his own expense. His younger brother George W. Imboden had followed his path into the law and also joined the artillery militia. Despite his lack of formal military training, J.D. Imboden was commissioned as captain in the Staunton Artillery of the Virginia State Militia on November 28, 1859.

Secession and Civil War
As the Virginia Secession Convention of 1861 appeared ready to pass the ordinance of secession, ex-Governor Henry Wise called a meeting of several militia commanders, Capt. A.M. Barbour (former civil superintendent of the Harper's Ferry Arsenal) and Richmond Enquirer editor Nat Tyler, to plan to capture the U.S. Army arsenal at Harpers Ferry. Imboden attended, as did Capt. John A. Harman of Staunton's infantry militia, as well as cavalry militia captains Turner and Richard Ashby of Fauquier County and Oliver R. Funsten of Clarke County. Imboden telegraphed Staunton's militia companies to meet in Staunton at 4p.m. on April 17 for orders. The group arranged railroad transportation to Charles Town about 8 miles from the Harpers Ferry arsenal. However, Governor John Letcher refused to authorized the action until the secession ordinance passed. As soon as it did, A.M. Barbour went to Harpers Ferry and publicly informed the workmen. Then, the evening after the secession ordinance passed, 360 men started out to capture the arsenal, defended by U.S. Army Lt. Roger Jones with 50 soldiers and about 15 volunteers. Vastly outnumbered, they attempted to set fire to the buildings and equipment as they retreated north to Carlisle, Pennsylvania. Imboden's artillery had set up on the heights above the arsenal, but did not see further action, although Imboden later bragged about converting some of the captured horse carts to caissons. Virginia militia Major General Kenton Harper (of Staunton) then secured the towns, and on April 27, Major Stonewall Jackson arrived to organize the militia into regiments.

On July 1, 1861, the Staunton Light Artillery, with its four bronze, 6-pounder guns and 107 officers and men, was formally mustered in the Confederate States Army. While commanding his artillery battery at the First Battle of Bull Run, Imboden perforated his left eardrum firing an artillery piece, causing subsequent deafness in that ear. He fought with Maj. Gen. Thomas J. "Stonewall" Jackson in the Valley Campaign at Cross Keys and Port Republic. On September 9, 1862, Imboden left the artillery to recruit a battalion of partisan rangers and was promoted to colonel of the 62nd Virginia Mounted Infantry (1st Partisan Rangers). He was promoted to brigadier general on January 28, 1863.

Upset by the independence movement in West Virginia, the voters of which petitioned to separate from Virginia, Imboden with Brig. Gen. William E. "Grumble" Jones led 3,400 partisan rangers into northwestern Virginia. The Jones-Imboden Raid destroyed rail track and bridges of the Baltimore and Ohio Railroad, as well as captured thousands of horses and heads of cattle and ruined petroleum fields in the Kanawha Valley. The raiders covered 400 mi in 37 days but failed to stop West Virginia's statehood.

In the Gettysburg Campaign, Imboden's brigade served under Maj. Gen. J.E.B. Stuart guarding the left flank for Gen. Robert E. Lee's movement north through the Shenandoah Valley. (His brigade did not participate in Stuart's foray away from Lee's army, but instead raided the Baltimore and Ohio Railroad between Martinsburg, West Virginia, and Cumberland, Maryland. ) During the Battle of Gettysburg, Imboden's men stayed in the rear and guarded ammunition and supply trains in Chambersburg, Pennsylvania. During the Confederate retreat, Imboden escorted the wagon trains with thousands of wounded soldiers back to Virginia. On July 6, 1863, the Potomac River flooding at Williamsport, Maryland, trapped Imboden's wagon train. He put together a defensive force that included an artillery battery and as many of the wounded who could operate muskets. This hastily organized force turned back attacks from Union cavalry generals John Buford and Judson Kilpatrick, saving the wagon train. Robert E. Lee praised Imboden for the way in which he "gallantly repulsed" the Union cavalry.

Returning to the Shenandoah Valley, Imboden responded to a request from General Lee to distract the enemy in his front by leading a raid on the vulnerable Union detachment at Charles Town, West Virginia, on October 18, 1863 at the Battle of Charlestown. Imboden reported,

"The surprise was complete, the enemy having no suspicion of our approach until I had the town entirely surrounded. ... To my demand for a surrender Colonel Simpson requested an hour for consideration. I offered him five minutes, to which he replied, 'Take us if you can'. I immediately opened on the buildings with artillery at less than 200 yards, and with half a dozen shells drove out the enemy into the streets, when he formed and fled toward Harper's Ferry."

Union Brig. Gen. Jeremiah Cutler Sullivan soon sent a rescue column from nearby Harpers Ferry and drove Imboden back up the valley. Sullivan reported, "The cavalry came up with the enemy this side of Charlestown, and drove them through the town. Artillery coming up, drove them about 4 miles. A portion of infantry force ..., reaching them, the enemy were driven from every position they took, to near Berryville."

Imboden and John C. Breckinridge's forces defeated Union Maj. Gen. Franz Sigel's command at the Battle of New Market on May 15, 1864. He returned to Virginia and commanded a brigade in Maj. Gen. Robert Ransom's cavalry division of the Second Corps of the Army of Northern Virginia under Lt. Gen. Jubal A. Early in the Valley Campaigns of 1864, including the losses at the Battle of Fisher's Hill and the Battle of Cedar Creek. Incapacitated by typhoid fever by the autumn of 1864, Imboden left the active cavalry service.

Beginning on January 2, 1865, Imboden commanded Camp Millen, Georgia, then the prison camp at Aiken, South Carolina as well as other prison camps in Georgia, Alabama, and Mississippi throughout 1865 until the end of the war. He was paroled in Augusta, Georgia on May 3 of that year.

Postbellum
After the war, Imboden moved to Richmond, Virginia, where he resumed his work as a lawyer, serving first in Richmond and then in Abingdon, the county seat of Washington County. His brother George moved to West Virginia, where he invested in land and coal, becoming a prominent businessman and legislator. John Imboden also advocated development of Virginia's natural resources and transportation infrastructure. In 1872 he published The Coal and Iron Resources of Virginia: Their Extent, Commercial Value, and Early Development Considered. Around 1875, he moved to southwestern Virginia where he hoped to mine coal and iron ore deposits. He founded the town of Damascus, Virginia, which became a lumber center in the late 19th and early 20th century. John D. Imboden also participated in Confederate veterans activities and the Lost Cause movement. He published several articles and books about the Civil War, and also contributed to The War of the Rebellion: A Compilation of the Official Records of the Union and Confederate Armies (128 vols., 1880–1901).

In 1876, he became a commissioner of the Centennial International Exposition in Philadelphia, Pennsylvania, and in 1893, he was a commissioner of the World's Columbian Exposition in Chicago. In the winter of 1879-1880, Imboden traveled to Pittsburgh, Pennsylvania, to meet with potential investors for coal operations in Wise County, Virginia. He persuaded the investors to begin operations with the region, leading to the chartering of the Tinsalia Coal Company, and later the Virginia Coal & Iron Company. Imboden then worked as a land agent for the company, securing property and mineral rights. One of the coal camps in Wise County, Imboden, is named after him.

Death and legacy
Imboden died in Damascus in 1895, and is buried in the Generals section of Hollywood Cemetery in Richmond, Virginia.

Family
During his life, Imboden was married five times; five out of his nine children were alive at the time of his death in 1895. On June 16, 1845, Imboden married Eliza "Dice" Allen McCue, who was a daughter of Colonel Franklin McCue. The Imbodens built a house in Staunton which they called the "Ingleside Cottage". They had four children, one died before reaching three years. On December 23, 1857, his wife of twelve years died. On May 12, 1859, Imboden married Mary Wilson McPhail, who gave birth to three children. Later, he married Edna Porter, then Anna Lockett, and finally Florence Crockett.

Works

 * Imboden, John D. Organized and Authorized Partisan Rangers. Staunton, Va., 1862. (Recruiting pamphlet)
 * Imboden, John D. Virginia, the Home for the Northern Farmer: Three Letters from Gen. J.d. Imboden, Domestic Agent of Immigration for the State of Virginia, to Hon. Horace Greeley. New York: D. Taylor, 1869.
 * Imboden, John D. Lee at Gettysburg. New York, 1871.
 * Imboden, John D. Reminiscences of Lee and Jackson. New York, 1871.
 * Imboden, John D. The Coal and Iron Resources of Virginia: Their Extent, Commercial Value, and Early Development Considered. a Paper Read Before a Meeting of Members of the Legislature and Prominent Citizens in the Capitol at Richmond, February 19th, 1872. Richmond: Clemmitt & Jones, printers, 1872.
 * Imboden, John D. Important to All Interested in Virginia, U.S. London: Foreign and Colonial Estates Exchange Agency, 1873.
 * Imboden, John D. Jackson at Harper's Ferry in 1861. In Battles and Leaders of the Civil War, ed. by Robert U. Johnson and Clarence C. Buel (1884–1887). 1.1 (1884): 111-125.
 * Imboden, John D. Coal and Coke: Coal Interests of South Western Virginia. s.l., 1894.

title: John D. Imboden


text: Wikipedia:WikiProject Utah/County template


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Training, validation, and test data sets

In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.

The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the corresponding output vector (or scalar), where the answer key is commonly denoted as the target (or label). The current model is run with the training data set and produces a result, which is then compared with the target, for each input vector in the training data set. Based on the result of the comparison and the specific learning algorithm being used, the parameters of the model are adjusted. The model fitting can include both variable selection and parameter estimation.

Successively, the fitted model is used to predict the responses for the observations in a second data set called the validation data set. The validation data set provides an unbiased evaluation of a model fit on the training data set while tuning the model's hyperparameters (e.g. the number of hidden units—layers and layer widths—in a neural network ). Validation data sets can be used for regularization by early stopping (stopping training when the error on the validation data set increases, as this is a sign of over-fitting to the training data set). This simple procedure is complicated in practice by the fact that the validation data set's error may fluctuate during training, producing multiple local minima. This complication has led to the creation of many ad-hoc rules for deciding when over-fitting has truly begun.

Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set. If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test set" in some literature (e.g., if the original data set was partitioned into only two subsets, the test set might be referred to as the validation set).

Deciding the sizes and strategies for data set division in training, test and validation sets is very dependent on the problem and data available.

Training data set
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier.

For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using “new” examples from the held-out data sets (validation and test data sets) to estimate the model’s accuracy in classifying new data. To reduce the risk of issues such as over-fitting, the examples in the validation and test data sets should not be used to train the model.

Most approaches that search through training data for empirical relationships tend to overfit the data, meaning that they can identify and exploit apparent relationships in the training data that do not hold in general.

Validation data set
A validation data set is a data set of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set". An example of a hyperparameter for artificial neural networks includes the number of hidden units in each layer. It, as well as the testing set (as mentioned below), should follow the same probability distribution as the training data set.

In order to avoid overfitting, when any classification parameter needs to be adjusted, it is necessary to have a validation data set in addition to the training and test data sets. For example, if the most suitable classifier for the problem is sought, the training data set is used to train the different candidate classifiers, the validation data set is used to compare their performances and decide which one to take and, finally, the test data set is used to obtain the performance characteristics such as accuracy, sensitivity, specificity, F-measure, and so on. The validation data set functions as a hybrid: it is training data used for testing, but neither as part of the low-level training nor as part of the final testing.

The basic process of using a validation data set for model selection (as part of training data set, validation data set, and test data set) is:

"Since our goal is to find the network having the best performance on new data, the simplest approach to the comparison of different networks is to evaluate the error function using data which is independent of that used for training. Various networks are trained by minimization of an appropriate error function defined with respect to a training data set. The performance of the networks is then compared by evaluating the error function using an independent validation set, and the network having the smallest error with respect to the validation set is selected. This approach is called the hold out method. Since this procedure can itself lead to some overfitting to the validation set, the performance of the selected network should be confirmed by measuring its performance on a third independent set of data called a test set."

An application of this process is in early stopping, where the candidate models are successive iterations of the same network, and training stops when the error on the validation set grows, choosing the previous model (the one with minimum error).

Test data set
A test data set is a data set that is independent of the training data set, but that follows the same probability distribution as the training data set. If a model fit to the training data set also fits the test data set well, minimal overfitting has taken place (see figure below). A better fitting of the training data set as opposed to the test data set usually points to over-fitting.

A test set is therefore a set of examples used only to assess the performance (i.e. generalization) of a fully specified classifier. To do this, the final model is used to predict classifications of examples in the test set. Those predictions are compared to the examples' true classifications to assess the model's accuracy.

In a scenario where both validation and test data sets are used, the test data set is typically used to assess the final model that is selected during the validation process. In the case where the original data set is partitioned into two subsets (training and test data sets), the test data set might assess the model only once (e.g., in the holdout method). Note that some sources advise against such a method. However, when using a method such as cross-validation, two partitions can be sufficient and effective since results are averaged after repeated rounds of model training and testing to help reduce bias and variability.

Confusion in terminology
Testing is trying something to find out about it ("To put to the proof; to prove the truth, genuineness, or quality of by experiment" according to the Collaborative International Dictionary of English) and to validate is to prove that something is valid ("To confirm; to render valid" Collaborative International Dictionary of English). With this perspective, the most common use of the terms test set and validation set is the one here described. However, in both industry and academia, they are sometimes used interchanged, by considering that the internal process is testing different models to improve (test set as a development set) and the final model is the one that needs to be validated before real use with an unseen data (validation set). "The literature on machine learning often reverses the meaning of 'validation' and 'test' sets. This is the most blatant example of the terminological confusion that pervades artificial intelligence research." Nevertheless, the important concept that must be kept is that the final set, whether called test or validation, should only be used in the final experiment.

Cross-validation
In order to get more stable results and use all valuable data for training, a data set can be repeatedly split into several training and a validation data sets. This is known as cross-validation. To confirm the model's performance, an additional test data set held out from cross-validation is normally used.

Causes of error
Omissions in the training of algorithms are a major cause of erroneous outputs. Types of such omissions include: An example of an omission of particular circumstances is a case where a boy was able to unlock the phone because his mother registered her face under indoor, nighttime lighting, a condition which was not appropriately included in the training of the system.
 * Particular circumstances or variations were not included.
 * Obsolete data
 * Ambiguous input information
 * Inability to change to new environments
 * Inability to request help from a human or another AI system when needed

Usage of relatively irrelevant input can include situations where algorithms use the background rather than the object of interest for object detection, such as being trained by pictures of sheep on grasslands, leading to a risk that a different object will be interpreted as a sheep if located on a grassland.


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Suzana Alves

Suzana Ferreira Alves or Tiazinha ("Auntie") (born São Paulo, August 3, 1978) is a major Brazilian sex symbol. The issue of the Brazilian Playboy that featured Tiazinha is one of the best-selling ones of all times for the Brazilian edition of the magazine.

She started as a dancer in the TV-show H at Rede Bandeirantes. The "Auntie" character was a dominatrix woman dressed in lingerie that would dance and be part of a game where Suzana would wax male participants in a live stage show. The participants would answer general knowledge questions, if the answer was incorrect they would be punished with waxing; if correct they would be rewarded with money prizes and lap dances from "Auntie".

She also released an album and starred her own action adventure TV-series, As Aventuras de Tiazinha (The Adventures of Tiazinha).

Ever since her departure from H in 2000, Alves has invested in her acting career, mostly on stage. She appeared in four episodes of Amigas & Rivais during 2007.

Alves is married since 2010 to retired tennis player Flávio Saretta, with whom she has a son.

title: Suzana Alves


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Talk:Guilt (emotion)

Neurological Section?
At a glance it looks to me like a total conjecture on a new and undeveloped subject. If there isn't a citation on it, it should be removed.

Objective Guilt
This article fails to discuss objective guilt (guilt in the sense of actual violation of moral law), and only considers the concept as an emotional (or subjective) state. There is a Guilt (law) section, but it only deals with guilt in the context of criminal law, and societal standards. Should another article be added, or should this article be expanded to consider guilt feelings as a reflection of the incurring of objective guilt through transgression of moral law? —Preceding unsigned comment added by 98.233.57.240 (talk) 00:48, 23 April 2010 (UTC)

people unable to feel guilt...
'Psychopaths' (and sociopaths) are now called Anti-Social Personality Disorder, so that last section should probably be update to the current term. Also maybe add something about Narcissistic Personality Disorder, who also either don't feel guilt, or experience it as shame. —Preceding unsigned comment added by 208.100.206.240 (talk) 04:54, 29 December 2007 (UTC)

- This is mostly incorrect. The term "Psychopath" and "Psychopathy" are still widely used in academic literature. Antisocial Personality Disorder (APD) shares overlapping characteristics with psychopathy, but in both forensic and clinical psychology the two are still regarded as distinct constructs. Think of it in a 'All medicines are drugs, but not all drugs are medicines' kind of way. Sociopath is an outdated term, however it also cannot be subsumed into APD. — Preceding unsigned comment added by 2A00:23C5:3D5C:3500:A13B:F0F:E8D1:701D (talk) 10:22, 27 September 2019 (UTC)

wouldn't that apply to just about anything
"a concept used in various ways in various contexts"...wouldn't that apply to just about anything? —Preceding unsigned comment added by 64.107.41.228 (talk • contribs)

Guilt has such a negative connotation
Guilt has such a negative connotation that it's often hard to accept it has a very necessary advisory role. Of course being guilty and feeling guilty are two different pebbles of the same colour. In the same manner we have an oil light on the dash of our car that comes on to warn us of possible damage if ignored, the feeling of guilt that can come over us is only a warning light and is as practical and necessary as, say, the feeling of hunger, which is to let us know it's time to take in sustenance. The feeling of guilt comes about when we wish to advise ourselves that there is a problem with integrating ideas, in other words, we've just compromised our values. The feeling of guilt will persist until the problem is addressed through change. It is not meant to indicate right or wrong, its role is only to advise a conflict of values has been introduced. —Preceding unsigned comment added by 70.68.202.155 (talk • contribs)

This needs two articles
This needs two articles. It appears there are at least a couple of very different kinds of guilt, psychological and legal. Are there any others? 2 August 2005 —Preceding unsigned comment added by Mneumisi (talk • contribs)

 * I concur. The section on legal guilt should be divorced from the psycology. The second paragraph in the "legal" section should also be eliminated or moved. 19 June 2006 —Preceding unsigned comment added by Mneumisi (talk • contribs)

 * Beautiful Mneumisi is responding to his own message, intentional or unintentional? Travb (talk) 10:12, 11 July 2006 (UTC)

 * Well if you can't agree with yourself, you probably shouldn't make comments. I would have to say "unintentional," since they were a year apart and unsigned at that. I wonder if I am conducting conversations with myself on any other talk pages? Still, Once again I have to agree with myself. Manney 13:59, 26 July 2006 (UTC)

 * I disagree. Rather, a better discussion of the scope of 'types of guilt' should replace this section. Many educational institutions discuss guilt as "collective" (sometimes called shared), "private," and "public." This is merely an observation from the discourse I've had on the topic relating to literature and film examples. Lepercon (talk) 05:39, 10 March 2008 (UTC)

 * I agree that this article could be split into more sections than just "guilt" as a general topic. Subsections would be a terrific idea for this topic.  I think another guilt type could be "moral guilt."  — Preceding unsigned comment added by 69.169.160.114 (talk) 02:05, 31 March 2012 (UTC)

Who writes the emotions articles?
Has anyone else noticed that people writing the emotions articles seem to exhibit the emotions they are writing about? For example, check out the ranting in the discussion group for hate, as opposed to say the joy people or the guilt people. 26 August 2005; —Preceding unsigned comment added by 216.254.17.226 (talk • contribs)
 * Hey, editors are human too! Jacobisq (talk) 10:43, 5 September 2011 (UTC)

Guilt's regret link ... to the race horse?
Guilt links to regret -- which is about a horse. I doubt this was intended. 27 November 2005; —Preceding unsigned comment added by 66.68.140.70 (talk • contribs)

Link to Regret/Section on Criminal Guilt
re: the above anonymous comment: Apparently, this has been corrected. However, someone knowledgeable needs to edit the section regarding criminal law in this article on Guilt. It begins with a incoherent sentence. soverman 21:06 16 Feb 2006 (UTC) —Preceding unsigned comment added by 24.164.205.69 (talk • contribs)

Added weasel words template
I added the weasel words template. "Some people..." etc. is not up to encyclopedic standards. Whenever I see "some people" the obvious question is who?

The more I read this article the less I like it. It needs a lot of work, but I am not interested enough in the subject to spend my time doing it.

I also added the tag.

ALSO: PLEASE SIGN YOUR POSTS using ~ Travb (talk) 10:07, 11 July 2006 (UTC)

Guilt Trip
Guilt trip is the act of making someone feel guily, not guilt itself; so I don't think the entry's should have been merged. --Spooony 20:49, 11 July 2006 (UTC)

criminal law guilt
I punched up a major revision of that section. I also considered a wholsale deleting of "causes of guilt" section since it is all weasel words without citation, but it is a start. Perhaps later. I used legal sources, for which there are very few, to define a concept which is taken for granted by just about everyone. I wanted to avoid philosophy, but I wanted more than a circular argument which required mentioning the idea of free will. Manney 15:06, 26 July 2006 (UTC)

Bag of fucking bricks
I'd love to include the line "guilt is like a bag of fucking bricks -- all you gotta do it set it down" from The Devil's Advocate movie. I think it's somewhere in between the psychopath and "dealing" section, but I can't find a place to put it. I think it represents a prominent, current view despite opinion.

May-be Guilty by association should be a disambiguation page
Guilty by association currently redirects here, but I think it could as well redirect to collective guilt. Maybe it needs a disambiguation page with links to both these articles.80.235.69.196 11:48, 29 October 2007 (UTC)
 * Sorry, this was meant for another discussion page (Association fallacy).80.235.69.196 11:50, 29 October 2007 (UTC)
 * Why does it redirect here? "Guilt by association" is a logical fallacy, and belongs within that catagory. 68.209.235.149 15:39, 14 November 2007 (UTC)

Distinction Between Guilt and Shame
There doesn't seem to a distinction between guilt and shame. If you read and compare the wikipedia articles on "guilt society" and "shame society" you will notice that there is a clear distinction drawn. It is almost revealed here on the "guilt" page:

"The philosopher Martin Buber underlined the difference between the Freudian notion of guilt, based on internal conflicts, and existential guilt, based on actual harm done to others."

This concept of "existential guilt" is shame, and the notion of guilt, based on internal conflict and the premonition of doing something sinful are quite different. In a shame culture, you judge your actions in the eyes of others. In a guilt culture, you judge your desires through the lens of social morality. Guilt has more to do with this internal conflict and psychological morality. (A Thought Surgeon Named Crisis (talk) 16:29, 2 November 2008 (UTC))

Add the phrase 'guilt-tripping'?
From http://www.rickross.com/reference/brainwashing/brainwashing11.html

Guilt-tripping – One thing that aggressive personalities know well is that other types of persons have very different consciences than they do. Manipulators are often skilled at using what they know to be the greater conscientiousness of their victims as a means of keeping them in a self-doubting, anxious, and submissive position. The more conscientious the potential victim, the more effective guilt is as a weapon. Aggressive personalities of all types use guilt-tripping so frequently and effectively as a manipulative tactic, that I believe it illustrates how fundamentally different in character they are compared to other (especially neurotic) personalities. All a manipulator has to do is suggest to the conscientious person that they don't care enough, are too selfish, etc., and that person immediately starts to feel bad. On the contrary, a conscientious person might try until they're blue in the face to get a manipulator (or any other aggressive personality) to feel badly about a hurtful behavior, acknowledge responsibility, or admit wrongdoing, to absolutely no avail.

- Nearfar (talk) 21:31, 22 December 2008 (UTC)

Removed split section tags, added in July 2007
I Removed the split section tags, added in 13:36, 13 July 2007 over a year and a half ago. travb (talk) 14:58, 17 January 2009 (UTC)

Non-Human Guilt?
I think this would be an interesting secton to add. There must be some interesting studies of "guilt", or shame, in apes, and there's at least one showing that the "guilty look" a dog gives you is just a response to human cues (http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6T2J-4WGM4JN-D&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=f291ef36ea7128512261ffde08ed8ac8)GM Pink Elephant (talk) 21:42, 2 December 2009 (UTC)

Removed overguilt and underguilt section
Never heard of either. Is this a joke? The terms certainly don't rise to subject heading on this page.

MarkinBoston (talk) 02:24, 5 March 2010 (UTC)

In Our Time
Rich Farmbrough, 03:14, 16 September 2010 (UTC).

Legality vs. emotion
The lede starts off like it's going to talk about the legal concept, as in Guilt (law). But the rest of the article describes the psychological, i.e., emotional aspect.

I think this article needs more distinction between guilt as a legal concept and the emotional feeling. --Uncle Ed (talk) 20:51, 4 June 2011 (UTC)

Cultural Guilt
A great addition would be the fact that different cultures think of moral guilt differently. For example, some societies think that killing a person is not morally wrong, however others would feel guilty for killing someone, even if it were in self defense.

Status of guilt.
If guilt is not observable in a state of affairs, then it arises by ascription or utterance. Simply ascribing (i.e. writing to) guilt to B doesn't establish the state of affairs where B is guilty and therefore doesn't establish the truth of the sentence 'B is guilty' just as simply saying "The cat is sitting on the mat" doesn't establish the state of affairs where the cat is sitting on the mat and therefore doesn't establish the truth of the sentence 'The cat is sitting on the mat'. The process is one of magical incantation i.e. saying it's so makes it so. Ash138.217.214.135 (talk) 12:19, 23 August 2012 (UTC)

Guilt and anticipated punishment of a society
"The end is nigh" was used to communicate the idea that as a society we had transgressed and that we would receive a collective punishment. There were those recently who claimed that the New Orleans disaster was a divine punishment for sodomy in that city. I presume there is a name for this syndrome of anticipated punishment for a perceived collective guilt. It would be useful if this aspect of Guilt were added to this article.FreeFlow99 (talk) 13:48, 21 November 2013 (UTC)

You need better references. Please do your research. Guilt is not a feeling, it is a ethical premise, thus we get statements like, 'innocent until proven guilty.' Also, there is real guilt, the proof of wrongdoing, and there is unearned guilt, the inappropriate feeling of fear associated with the pathological distortion of having done something wrong, which can't be proven. The feeling associated with "guilt" is fear. Guilt is not a feeling, but fear is. We say, I feel guilty, but the psychology behind it is, "I am afraid something bad might happen to me, like God's retribution, or a judge of law will hurt me. Also, this is not a debate. I apologize for not referencing what I said, but I don't have the time. I read this and needed to say, Please DO YOUR RESEARCH! Thank you. David Springer PhD. DD.

Tribe?
It says tribe, but shouldn't it be changed to social circle / family? Or group? Tribe sounds off to me i dont know.

2001:44B8:4137:6200:A5F5:7487:198D:674C (talk) 06:10, 1 September 2018 (UTC) Anon

Other uses: Song by Eminem
Hello,

the search term "Guilty Conscience" redirects to this page. It also is a song by Eminem and Dr. Dre, but is neither mention in the disambugations for "Guilt" or "Guilty". Since the song as an rather comprehensive article I'd like to add it into the "For other uses, see ..." section but I don't know how. Would be great if someone could do that.

SkySilver (talk) 02:48, 24 February 2019 (UTC)

Psychodynamic Defence Theory is Being Presented as Fact
I have some issues with the "Defences" section in that it is almost all written from the perspective of the psychodynamic/psychoanalytic theorists. These theories (regardless of citation origin) should be prefaced with information stating that they are the view of the aforementioned school of psychotherapy and are contested.

"Lack of guilt in psychopaths" listed at Redirects for discussion
A discussion is taking place to address the redirect Lack of guilt in psychopaths. The discussion will occur at Redirects for discussion/Log/2021 November 26 until a consensus is reached, and readers of this page are welcome to contribute to the discussion. Marcocapelle (talk) 12:59, 26 November 2021 (UTC)

etymology
The Online Etymology Dictionary says that the derivation of gylt from gieldan is suspect. 2605:A601:A73A:7900:300D:B32E:F400:FC63 (talk) 01:39, 13 March 2022 (UTC)

title: Talk:Guilt (emotion)


text:
Gedney, Lincolnshire

Gedney is a village, civil parish and electoral ward in the South Holland district of Lincolnshire, England. It is just to the south of the A17 Boston to King's Lynn road, 2 mi east from Holbeach and 2 mi north-west from Long Sutton. The parish stretches east to The Wash, its villages and hamlets including Dawsmere, Gedney Broadgate, Gedney Drove End, Gedney Dyke, Gedney Marsh, and the geographic extension of Gedney Church End.

History
A hospital for five paupers, (St Thomas Martyr), was founded at Gedney, date unknown, and served from North Creake. It was dissolved around 1339.

The redundant railway station was on the former east–west Midland and Great Northern Joint Railway. It closed in 1959.

The Red House wind farm was built in 2006 with six MM82 wind turbines, its 12MW of power feeding 6,500 homes.

Geography
Gedney and its parish lies on reclaimed fenland, making it one of the most intensive crop-growing areas in the UK.

To the west, the parish begins at the eastern end of the Fleet Hargate bypass, and includes the village's campsite and a few houses, bordering the parish of Fleet. The boundary passes close to the west of Gedney Dyke, and meets the parish of Holbeach. It passes just to the west of Gedney Dyke Farm then follows Fleet Haven, near to the wind farm to the west of nearby Red House Farm.

It deviates to the east from Fleet Haven and passes to the west of (another) Red House Farm then rejoins Fleet Haven just south of Wards Farm. Fleet Haven runs out of land and meets the sea wall. In the parish, and close to the east is the base of RAF Holbeach which is accessed via Durham's Road. The parish extends out on the mud flats along Fleet Haven Outfall. To access the part of the parish along the start of The Wash would be extremely foolish and is out of bounds due to the bombing range. The limit of the parish finishes between Gat Sea End and Old South Middle. There are observation towers on the sea wall for the bombing range. The control tower is on the sea wall towards Dawsmere and Gedney Drove End. Dawsmere Creek extends out towards The Wash. The boundary with Lutton is along Boatmere Creek then extends out to sea along Tycho Wing's Channel, which is the outflow of the River Nene. This area is the far west of The Wash NNR.

The boundary passes to the north of Lodge Farm, broadly following Boatmere Creek, south of Boat Mere Farm then along The Chase. It crosses Roman Bank at Sutton Corner, passing to the west of Lutton. It follows Docking's Holt Lane to the west of Long Sutton, meeting the B1359 at the Garnsgate Road junction. It follows the B1359 (former A17) to the west, then crosses the A17 then follows a drain and Garnsgate Road where it borders Long Sutton and partly follows National Cycle Route 1. It passes to the north of Moorswood Farm, then follows Benderslough Drove and then the South Holland Main Drain, where it borders Sutton St James to the south. At Leedsgate Bridge, west of Inley Drove Farm, it meets the parish of Fleet.

North of Leedsgate Bridge, the parish follows Fleet Drain, to the left of Rookery Farm, and it crosses the B1165 and the Little South Holland Drain at Raven's Gate Bridge. The boundary follows Fleet Drain north-eastwards alongside Raven's Drove. The two sides of the boundary are respectively known as Fleet Fen and Gedney Fen. Fleet Drain also follows Delph Bank. To the east of Fleet Drain at this point is Gedney Broadgate.

Church
Gedney Grade I listed Anglican parish church is dedicated to St Mary Magdalene. Originating in the 13th century, additions and alterations carried-on into the 17th. It was considerably restored in 1890 however the spire to the tower was unfinished and left as a stump. The tower, 86 ft to its parapet, has Early English lower stages and Perpendicular upper. The nave arcades and the chancel are of Decorated style. During the rebuilding of the south aisle in 1890 a brass of a female (ca. 1390), with a puppy at her feet, was discovered. Also in the south aisle is a damaged 13th-century effigy of a cross-legged knight, conjectured to represent Falco D'Oyry, and Jacobean alabaster monuments of Adlard Welby, his wife Cassandra and their five children, erected in 1605. The south porch has a rare upper chamber. At the east end of the north aisle are the remains of a 14th-century Jesse window.

The church had 29 windows damaged in 2006, including the Jesse window. Vestments and an altar cloth were set alight but the fire did not take hold. Two culprits, 15 and 16 years old, were interviewed by police and admitted causing the damage.

Amenities
Parish public houses are the Chequers on Gedney Dyke Main Street, the Grade II listed Old Black Lion in Chapelgate, and the Rising Sun.

title: Gedney, Lincolnshire


text:
User:Femto/elements e1

 * Question: Why do the element symbols in the isotopes section link to that element. People will already be on the element's page when they're merged. Fresheneesz 17:53, 26 May 2006 (UTC)

 * What links where? You do realize this is only an old working page? Femto 18:06, 26 May 2006 (UTC)

hydrogen

helium

lithium

beryllium

boron

carbon

nitrogen

oxygen

fluorine

neon

title: User:Femto/elements e1


text:
Instrumental variables estimation

In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term (endogenous), in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable (is correlated with the endogenous variable) but has no independent effect on the dependent variable and is not correlated with the error term, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable.

Instrumental variable methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur when: Explanatory variables that suffer from one or more of these issues in the context of a regression are sometimes referred to as endogenous. In this situation, ordinary least squares produces biased and inconsistent estimates. However, if an instrument is available, consistent estimates may still be obtained. An instrument is a variable that does not itself belong in the explanatory equation but is correlated with the endogenous explanatory variables, conditionally on the value of other covariates.
 * 1) changes in the dependent variable change the value of at least one of the covariates ("reverse" causation),
 * 2) there are omitted variables that affect both the dependent and explanatory variables, or
 * 3) the covariates are subject to non-random measurement error.

In linear models, there are two main requirements for using IVs:
 * The instrument must be correlated with the endogenous explanatory variables, conditionally on the other covariates. If this correlation is strong, then the instrument is said to have a strong first stage. A weak correlation may provide misleading inferences about parameter estimates and standard errors.
 * The instrument cannot be correlated with the error term in the explanatory equation, conditionally on the other covariates. In other words, the instrument cannot suffer from the same problem as the original predicting variable. If this condition is met, then the instrument is said to satisfy the exclusion restriction.

Example
Informally, in attempting to estimate the causal effect of some variable X ("covariate" or "explanatory variable") on another Y ("dependent variable"), an instrument is a third variable Z which affects Y only through its effect on X.

For example, suppose a researcher wishes to estimate the causal effect of smoking (X) on general health (Y). Correlation between smoking and health does not imply that smoking causes poor health because other variables, such as depression, may affect both health and smoking, or because health may affect smoking. It is not possible to conduct controlled experiments on smoking status in the general population. The researcher may attempt to estimate the causal effect of smoking on health from observational data by using the tax rate for tobacco products (Z) as an instrument for smoking. The tax rate for tobacco products is a reasonable choice for an instrument because the researcher assumes that it can only be correlated with health through its effect on smoking. If the researcher then finds tobacco taxes and state of health to be correlated, this may be viewed as evidence that smoking causes changes in health.

History
First use of an instrument variable occurred in a 1928 book by Philip G. Wright, best known for his excellent description of the production, transport and sale of vegetable and animal oils in the early 1900s in the United States, while in 1945, Olav Reiersøl applied the same approach in the context of errors-in-variables models in his dissertation, giving the method its name.

Wright attempted to determine the supply and demand for butter using panel data on prices and quantities sold in the United States. The idea was that a regression analysis could produce a demand or supply curve because they are formed by the path between prices and quantities demanded or supplied. The problem was that the observational data did not form a demand or supply curve as such, but rather a cloud of point observations that took different shapes under varying market conditions. It seemed that making deductions from the data remained elusive.

The problem was that price affected both supply and demand so that a function describing only one of the two could not be constructed directly from the observational data. Wright correctly concluded that he needed a variable that correlated with either demand or supply but not both – that is, an instrumental variable.

After much deliberation, Wright decided to use regional rainfall as his instrumental variable: he concluded that rainfall affected grass production and hence milk production and ultimately butter supply, but not butter demand. In this way he was able to construct a regression equation with only the instrumental variable of price and supply.

Formal definitions of instrumental variables, using counterfactuals and graphical criteria, were given by Judea Pearl in 2000. Angrist and Krueger (2001) present a survey of the history and uses of instrumental variable techniques. Notions of causality in econometrics, and their relationship with instrumental variables and other methods, are discussed by Heckman (2008).

Theory
While the ideas behind IV extend to a broad class of models, a very common context for IV is in linear regression. Traditionally, an instrumental variable is defined as a variable Z that is correlated with the independent variable X and uncorrelated with the "error term" U in the linear equation

 * $$Y = X \beta + U $$

$$Y$$ is a vector. $$X$$ is a matrix, usually with a column of ones and perhaps with additional columns for other covariates. Consider how an instrument allows $$\beta$$ to be recovered. Recall that OLS solves for $$ \widehat{\beta }$$ such that $$\operatorname{cov}(X,\widehat U) = 0$$ (when we minimize the sum of squared errors, $$\min_\beta (Y- X\beta)'(Y- X\beta) $$, the first-order condition is exactly $$ X' (Y- X\widehat{\beta}) = X' \widehat{U} = 0 $$.) If the true model is believed to have $$\operatorname{cov}(X,U) \neq 0$$ due to any of the reasons listed above—for example, if there is an omitted variable which affects both $$X$$ and $$Y$$ separately—then this OLS procedure will not yield the causal impact of $$X$$ on $$Y$$. OLS will simply pick the parameter that makes the resulting errors appear uncorrelated with $$X$$.

Consider for simplicity the single-variable case. Suppose we are considering a regression with one variable and a constant (perhaps no other covariates are necessary, or perhaps we have partialed out any other relevant covariates):

 * $$y=\alpha + \beta x + u$$

In this case, the coefficient on the regressor of interest is given by $$ \widehat{\beta }= \frac{\operatorname{cov}(x,y)}{\operatorname{var}(x)} $$. Substituting for $$y$$ gives

\begin{align} \widehat{\beta} & = \frac{\operatorname{cov}(x,y)}{\operatorname{var}(x)} = \frac{\operatorname{cov}(x,\alpha + \beta x + u)}{\operatorname{var}(x)} \\[6pt] & =\frac{\operatorname{cov}(x, \alpha +\beta x)}{\operatorname{var}(x)} +\frac{\operatorname{cov}(x,u)}{\operatorname{var}(x)}= \beta^* + \frac{\operatorname{cov}(x,u)}{\operatorname{var}(x)}, \end{align} $$

where $$\beta^*$$ is what the estimated coefficient vector would be if x were not correlated with u. In this case, it can be shown that $$\beta^*$$ is an unbiased estimator of $$\beta .$$ If $$\operatorname{cov}(x,u) \neq 0$$ in the underlying model that we believe, then OLS gives a coefficient which does not reflect the underlying causal effect of interest. IV helps to fix this problem by identifying the parameters $${\beta}$$ not based on whether $$x$$ is uncorrelated with $$u$$, but based on whether another variable $$z$$ is uncorrelated with $$u$$. If theory suggests that $$z$$ is related to $$x$$ (the first stage) but uncorrelated with $$u$$ (the exclusion restriction), then IV may identify the causal parameter of interest where OLS fails. Because there are multiple specific ways of using and deriving IV estimators even in just the linear case (IV, 2SLS, GMM), we save further discussion for the Estimation section below.

Graphical definition
IV techniques have been developed among a much broader class of non-linear models. General definitions of instrumental variables, using counterfactual and graphical formalism, were given by Pearl (2000; p. 248). The graphical definition requires that Z satisfy the following conditions:

 * $$(Z \perp\!\!\!\perp Y)_{G_{\overline{X}}} \qquad(Z \not\!\!{\perp\!\!\!\perp} X)_G $$

where $$\perp\!\!\!\perp$$ stands for d-separation and $$G_{\overline{X}}$$ stands for the graph in which all arrows entering X are cut off.

The counterfactual definition requires that Z satisfies

 * $$(Z \perp\!\!\!\perp Y_x)\qquad (Z \not\!\!{\perp\!\!\!\perp} X)$$

where Yx stands for the value that Y would attain had X been x and $$\perp\!\!\!\perp$$ stands for independence.

If there are additional covariates W then the above definitions are modified so that Z qualifies as an instrument if the given criteria hold conditional on W.

The essence of Pearl's definition is:
 * 1) The equations of interest are "structural", not "regression".
 * 2) The error term U stands for all exogenous factors that affect Y when X is held constant.
 * 3) The instrument Z should be independent of U.
 * 4) The instrument Z should not affect Y when X is held constant (exclusion restriction).
 * 5) The instrument Z should not be independent of X.

These conditions do not rely on specific functional form of the equations and are applicable therefore to nonlinear equations, where U can be non-additive (see Non-parametric analysis). They are also applicable to a system of multiple equations, in which X (and other factors) affect Y through several intermediate variables. An instrumental variable need not be a cause of X; a proxy of such cause may also be used, if it satisfies conditions 1–5. The exclusion restriction (condition 4) is redundant; it follows from conditions 2 and 3.

Selecting suitable instruments
Since U is unobserved, the requirement that Z be independent of U cannot be inferred from data and must instead be determined from the model structure, i.e., the data-generating process. Causal graphs are a representation of this structure, and the graphical definition given above can be used to quickly determine whether a variable Z qualifies as an instrumental variable given a set of covariates W. To see how, consider the following example.

Suppose that we wish to estimate the effect of a university tutoring program on grade point average (GPA). The relationship between attending the tutoring program and GPA may be confounded by a number of factors. Students who attend the tutoring program may care more about their grades or may be struggling with their work. This confounding is depicted in the Figures 1–3 on the right through the bidirected arc between Tutoring Program and GPA. If students are assigned to dormitories at random, the proximity of the student's dorm to the tutoring program is a natural candidate for being an instrumental variable.

However, what if the tutoring program is located in the college library? In that case, Proximity may also cause students to spend more time at the library, which in turn improves their GPA (see Figure 1). Using the causal graph depicted in the Figure 2, we see that Proximity does not qualify as an instrumental variable because it is connected to GPA through the path Proximity $$ \rightarrow $$ Library Hours $$ \rightarrow $$ GPA in $$G_{\overline{X}}$$. However, if we control for Library Hours by adding it as a covariate then Proximity becomes an instrumental variable, since Proximity is separated from GPA given Library Hours in $$G_{\overline{X}}$$.

Now, suppose that we notice that a student's "natural ability" affects his or her number of hours in the library as well as his or her GPA, as in Figure 3. Using the causal graph, we see that Library Hours is a collider and conditioning on it opens the path Proximity $$\rightarrow$$ Library Hours $$\leftrightarrow$$ GPA. As a result, Proximity cannot be used as an instrumental variable.

Finally, suppose that Library Hours does not actually affect GPA because students who do not study in the library simply study elsewhere, as in Figure 4. In this case, controlling for Library Hours still opens a spurious path from Proximity to GPA. However, if we do not control for Library Hours and remove it as a covariate then Proximity can again be used an instrumental variable.

Estimation
We now revisit and expand upon the mechanics of IV in greater detail. Suppose the data are generated by a process of the form

 * $$ y_i = X_i \beta + e_i, $$

where
 * i indexes observations,
 * $$y_i$$ is the i-th value of the dependent variable,
 * $$X_i$$ is a vector of the i-th values of the independent variable(s) and a constant,
 * $$e_i$$ is the i-th value of an unobserved error term representing all causes of $$y_i$$ other than $$X_i$$, and
 * $$\beta$$ is an unobserved parameter vector.

The parameter vector $$\beta$$ is the causal effect on $$y_i$$ of a one unit change in each element of $$X_i$$, holding all other causes of $$y_i$$ constant. The econometric goal is to estimate $$\beta$$. For simplicity's sake assume the draws of e are uncorrelated and that they are drawn from distributions with the same variance (that is, that the errors are serially uncorrelated and homoskedastic).

Suppose also that a regression model of nominally the same form is proposed. Given a random sample of T observations from this process, the ordinary least squares estimator is

 * $$ \widehat{\beta}_\mathrm{OLS} = (X^\mathrm T X)^{-1} X^\mathrm T y = (X^\mathrm T X)^{-1} X^\mathrm T (X \beta + e) = \beta + (X^\mathrm T X)^{-1} X^\mathrm T e$$

where X, y and e denote column vectors of length T. This equation is similar to the equation involving $$ \operatorname{cov}(X,y) $$ in the introduction (this is the matrix version of that equation). When X and e are uncorrelated, under certain regularity conditions the second term has an expected value conditional on X of zero and converges to zero in the limit, so the estimator is unbiased and consistent. When X and the other unmeasured, causal variables collapsed into the e term are correlated, however, the OLS estimator is generally biased and inconsistent for β. In this case, it is valid to use the estimates to predict values of y given values of X, but the estimate does not recover the causal effect of X on y.

To recover the underlying parameter $$ \beta $$, we introduce a set of variables Z that is highly correlated with each endogenous component of X but (in our underlying model) is not correlated with e. For simplicity, one might consider X to be a T × 2 matrix composed of a column of constants and one endogenous variable, and Z to be a T × 2 consisting of a column of constants and one instrumental variable. However, this technique generalizes to X being a matrix of a constant and, say, 5 endogenous variables, with Z being a matrix composed of a constant and 5 instruments. In the discussion that follows, we will assume that X is a T × K matrix and leave this value K unspecified. An estimator in which X and Z are both T × K matrices is referred to as just-identified.

Suppose that the relationship between each endogenous component xi and the instruments is given by

 * $$ x_i = Z_i \gamma + v_i, $$

The most common IV specification uses the following estimator:

 * $$ \widehat{\beta}_\mathrm{IV} = (Z^\mathrm T X)^{-1} Z^\mathrm T y $$

This specification approaches the true parameter as the sample gets large, so long as $$ Z^\mathrm T e = 0 $$ in the true model:

 * $$ \widehat{\beta}_\mathrm{IV} = (Z^\mathrm T X)^{-1} Z^\mathrm T y = (Z^\mathrm T X)^{-1} Z^\mathrm T X \beta + (Z^\mathrm T X)^{-1} Z^\mathrm T e \rightarrow \beta    $$

As long as $$ Z^\mathrm T e = 0 $$ in the underlying process which generates the data, the appropriate use of the IV estimator will identify this parameter. This works because IV solves for the unique parameter that satisfies $$ Z^\mathrm T e = 0 $$, and therefore hones in on the true underlying parameter as the sample size grows.

Now an extension: suppose that there are more instruments than there are covariates in the equation of interest, so that Z is a T × M matrix with M > K. This is often called the over-identified case. In this case, the generalized method of moments (GMM) can be used. The GMM IV estimator is

 * $$ \widehat{\beta}_\mathrm{GMM} = (X^\mathrm T P_Z X)^{-1}X^\mathrm T P_Z y,$$

where $$P_Z$$ refers to the projection matrix $$P_Z=Z(Z^\mathrm T Z)^{-1}Z^\mathrm T$$.

This expression collapses to the first when the number of instruments is equal to the number of covariates in the equation of interest. The over-identified IV is therefore a generalization of the just-identified IV.

Developing the $$\beta_\text{GMM}$$ expression:
 * $$ \widehat{\beta}_\mathrm{GMM} = (X^\mathrm{T} Z(Z^\mathrm{T} Z)^{-1}Z^\mathrm{T} X)^{-1}X^\mathrm{T} Z(Z^\mathrm{T} Z)^{-1}Z^\mathrm{T} y$$

In the just-identified case, we have as many instruments as covariates, so that the dimension of X is the same as that of Z. Hence, $$X^\mathrm{T} Z, Z^\mathrm{T} Z$$ and $$Z^\mathrm{T}X$$ are all squared matrices of the same dimension. We can expand the inverse, using the fact that, for any invertible n-by-n matrices A and B, (AB)−1 = B−1A−1 (see Invertible matrix):

\begin{align} \widehat{\beta}_\mathrm{GMM} &= (Z^\mathrm{T} X)^{-1}(Z^\mathrm{T} Z)(X^\mathrm{T} Z)^{-1}X^\mathrm{T} Z(Z^\mathrm{T} Z)^{-1}Z^\mathrm{T} y\\ &= (Z^\mathrm{T} X)^{-1}(Z^\mathrm{T} Z)(Z^\mathrm{T} Z)^{-1}Z^\mathrm{T} y\\ &=(Z^\mathrm{T} X)^{-1}Z^\mathrm{T}y \\ &=\widehat{\beta}_\mathrm{IV} \end{align} $$ Reference: see Davidson and Mackinnnon (1993)

There is an equivalent under-identified estimator for the case where m < k. Since the parameters are the solutions to a set of linear equations, an under-identified model using the set of equations $$ Z'v = 0 $$ does not have a unique solution.

Interpretation as two-stage least squares
One computational method which can be used to calculate IV estimates is two-stage least squares (2SLS or TSLS). In the first stage, each explanatory variable that is an endogenous covariate in the equation of interest is regressed on all of the exogenous variables in the model, including both exogenous covariates in the equation of interest and the excluded instruments. The predicted values from these regressions are obtained:

Stage 1: Regress each column of X on Z, ($$ X = Z \delta + \text{errors} $$):

 * $$\widehat{\delta}=(Z^\mathrm{T} Z)^{-1}Z^\mathrm{T}X, \,$$

and save the predicted values:

 * $$\widehat{X}= Z\widehat{\delta} = {\color{ProcessBlue}Z(Z^\mathrm{T} Z)^{-1}Z^\mathrm{T}}X = {\color{ProcessBlue}P_Z} X.\, $$

In the second stage, the regression of interest is estimated as usual, except that in this stage each endogenous covariate is replaced with the predicted values from the first stage:

Stage 2:  Regress Y on the predicted values from the first stage:

 * $$ Y = \widehat X \beta + \mathrm{noise},\,$$

which gives
 * $$ \beta_\text{2SLS} = \left(X^\mathrm{T}{\color{ProcessBlue}P_Z} X\right)^{-1} X^\mathrm{T}{\color{ProcessBlue}P_Z}Y.$$

This method is only valid in linear models. For categorical endogenous covariates, one might be tempted to use a different first stage than ordinary least squares, such as a probit model for the first stage followed by OLS for the second. This is commonly known in the econometric literature as the forbidden regression, because second-stage IV parameter estimates are consistent only in special cases.

The usual OLS estimator is: $$ (\widehat X^\mathrm{T}\widehat X)^{-1}\widehat X^\mathrm{T}Y$$. Replacing $$ \widehat X = P_Z X$$ and noting that $$P_Z $$ is a symmetric and idempotent matrix, so that $$ P_Z^\mathrm{T}P_Z=P_Z P_Z = P_Z$$
 * $$ \beta_\text{2SLS} = (\widehat X^\mathrm{T}\widehat X)^{-1}\widehat X^\mathrm{T} Y = \left(X^\mathrm{T}P_Z^\mathrm{T}P_Z X\right)^{-1} X^\mathrm{T}P_Z^\mathrm{T}Y=\left(X^\mathrm{T}P_Z X\right)^{-1} X^\mathrm{T}P_ZY.$$

The resulting estimator of $$\beta$$ is numerically identical to the expression displayed above. A small correction must be made to the sum-of-squared residuals in the second-stage fitted model in order that the covariance matrix of $$\beta$$ is calculated correctly.

Non-parametric analysis
When the form of the structural equations is unknown, an instrumental variable $$Z$$ can still be defined through the equations:

 * $$x = g(z,u) \, $$
 * $$y = f(x,u) \, $$

where $$f$$ and $$g$$ are two arbitrary functions and $$Z$$ is independent of $$U$$. Unlike linear models, however, measurements of $$Z, X$$ and $$Y$$ do not allow for the identification of the average causal effect of $$X$$ on $$Y$$, denoted ACE
 * $$\text{ACE} = \Pr(y\mid \text{do}(x)) = \operatorname{E}_u[f(x,u)].$$

Balke and Pearl [1997] derived tight bounds on ACE and showed that these can provide valuable information on the sign and size of ACE.

In linear analysis, there is no test to falsify the assumption the $$Z$$ is instrumental relative to the pair $$(X,Y)$$. This is not the case when $$X$$ is discrete. Pearl (2000) has shown that, for all $$f$$ and $$g$$, the following constraint, called "Instrumental Inequality" must hold whenever $$Z$$ satisfies the two equations above:
 * $$\max_x \sum_y [\max_z \Pr(y,x\mid z)]\leq 1.$$

Interpretation under treatment effect heterogeneity
The exposition above assumes that the causal effect of interest does not vary across observations, that is, that $$\beta$$ is a constant. Generally, different subjects will respond in different ways to changes in the "treatment" x. When this possibility is recognized, the average effect in the population of a change in x on y may differ from the effect in a given subpopulation. For example, the average effect of a job training program may substantially differ across the group of people who actually receive the training and the group which chooses not to receive training. For these reasons, IV methods invoke implicit assumptions on behavioral response, or more generally assumptions over the correlation between the response to treatment and propensity to receive treatment.

The standard IV estimator can recover local average treatment effects (LATE) rather than average treatment effects (ATE). Imbens and Angrist (1994) demonstrate that the linear IV estimate can be interpreted under weak conditions as a weighted average of local average treatment effects, where the weights depend on the elasticity of the endogenous regressor to changes in the instrumental variables. Roughly, that means that the effect of a variable is only revealed for the subpopulations affected by the observed changes in the instruments, and that subpopulations which respond most to changes in the instruments will have the largest effects on the magnitude of the IV estimate.

For example, if a researcher uses presence of a land-grant college as an instrument for college education in an earnings regression, she identifies the effect of college on earnings in the subpopulation which would obtain a college degree if a college is present but which would not obtain a degree if a college is not present. This empirical approach does not, without further assumptions, tell the researcher anything about the effect of college among people who would either always or never get a college degree regardless of whether a local college exists.

Weak instruments problem
As Bound, Jaeger, and Baker (1995) note, a problem is caused by the selection of "weak" instruments, instruments that are poor predictors of the endogenous question predictor in the first-stage equation. In this case, the prediction of the question predictor by the instrument will be poor and the predicted values will have very little variation. Consequently, they are unlikely to have much success in predicting the ultimate outcome when they are used to replace the question predictor in the second-stage equation.

In the context of the smoking and health example discussed above, tobacco taxes are weak instruments for smoking if smoking status is largely unresponsive to changes in taxes. If higher taxes do not induce people to quit smoking (or not start smoking), then variation in tax rates tells us nothing about the effect of smoking on health. If taxes affect health through channels other than through their effect on smoking, then the instruments are invalid and the instrumental variables approach may yield misleading results. For example, places and times with relatively health-conscious populations may both implement high tobacco taxes and exhibit better health even holding smoking rates constant, so we would observe a correlation between health and tobacco taxes even if it were the case that smoking has no effect on health. In this case, we would be mistaken to infer a causal effect of smoking on health from the observed correlation between tobacco taxes and health.

Testing for weak instruments
The strength of the instruments can be directly assessed because both the endogenous covariates and the instruments are observable. A common rule of thumb for models with one endogenous regressor is: the F-statistic against the null that the excluded instruments are irrelevant in the first-stage regression should be larger than 10.

Statistical inference and hypothesis testing
When the covariates are exogenous, the small-sample properties of the OLS estimator can be derived in a straightforward manner by calculating moments of the estimator conditional on X. When some of the covariates are endogenous so that instrumental variables estimation is implemented, simple expressions for the moments of the estimator cannot be so obtained. Generally, instrumental variables estimators only have desirable asymptotic, not finite sample, properties, and inference is based on asymptotic approximations to the sampling distribution of the estimator. Even when the instruments are uncorrelated with the error in the equation of interest and when the instruments are not weak, the finite sample properties of the instrumental variables estimator may be poor. For example, exactly identified models produce finite sample estimators with no moments, so the estimator can be said to be neither biased nor unbiased, the nominal size of test statistics may be substantially distorted, and the estimates may commonly be far away from the true value of the parameter.

Testing the exclusion restriction
The assumption that the instruments are not correlated with the error term in the equation of interest is not testable in exactly identified models. If the model is overidentified, there is information available which may be used to test this assumption. The most common test of these overidentifying restrictions, called the Sargan–Hansen test, is based on the observation that the residuals should be uncorrelated with the set of exogenous variables if the instruments are truly exogenous. The Sargan–Hansen test statistic can be calculated as $$TR^2$$ (the number of observations multiplied by the coefficient of determination) from the OLS regression of the residuals onto the set of exogenous variables. This statistic will be asymptotically chi-squared with m − k degrees of freedom under the null that the error term is uncorrelated with the instruments.

title: Instrumental variables estimation


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Category:Bays of Ireland

Bays of Ireland

title: Category:Bays of Ireland


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Talk:Mundelein College

Tone of the article
This all sounds incredibly promotional, as if it was lifted word for word from a website elsewhere.... 2603:8001:2A00:7428:2D3D:208B:90E9:27AE (talk) 01:57, 20 December 2022 (UTC)
 * Thanks for your suggestion. When you believe an article needs improvement, please feel free to change it. We encourage you to be bold in updating pages, because wikis like ours develop faster when everybody edits. Don't worry too much about making honest mistakes—they're likely to be found and corrected quickly. You can always preview your edits before you publish them or test them out in the sandbox. If you need additional help, check out our getting started page or ask the friendly folks at the Teahouse.   ElKevbo (talk) 03:12, 20 December 2022 (UTC)

title: Talk:Mundelein College


text: User talk:66.235.57.143

title: User talk:66.235.57.143


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User:Femto/elements e2

sodium

magnesium

aluminium

silicon

phosphorus

sulfur

chlorine

argon

title: User:Femto/elements e2


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Kirkby Moor

Kirkby Moor is a poorly defined moorland area in southern Cumbria, England, named after the village of Kirkby-in-Furness, but stretching both sides of the A5092 road, and thus spanning the border of the Lake District National Park. The highest area, to the south of the road, and therefore outside the national park, is named Lowick High Common on Ordnance Survey maps, but "Kirkby Moor" is more commonly but incorrectly used. Lowick High common is the first UK SOTA summit to be activated 100 times.

The highest point of Lowick High Common and of Kirkby Moor is 333 m above sea level. The major man-made features are a slate quarry operated by Burlington Slate Ltd and a wind farm consisting of 12 400 kW turbines which is operated by RWE Innogy.

The fell is crossed by several public footpaths, as well as the Kirkby Slate Road, which has a right of access. There are also numerous tracks built across the fell in association with the wind turbines; although notices request walkers to stick to the public rights of way, in practice there is no issue with using the newer tracks, and from 28 May 2005 the land is access land under the Countryside and Rights of Way Act 2000.

The wide range of paths allows for many start points and routes to explore the heather-covered moor.

title: Kirkby Moor


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User:Femto/elements e3

potassium

calcium

gallium

germanium

arsenic

selenium

bromine

krypton

title: User:Femto/elements e3


text: User talk:203.164.94.92

title: User talk:203.164.94.92


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User:Femto/elements e4

scandium

titanium

vanadium

chromium

manganese

iron

cobalt

nickel

copper

zinc

title: User:Femto/elements e4


text: User talk:204.184.202.13

title: User talk:204.184.202.13


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User:Femto/elements e5

rubidium

strontium

indium

tin

antimony

tellurium

iodine

xenon

title: User:Femto/elements e5


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Category:Politician stubs

Due to the large size of this category, please use more specific templates where possible
Most countries have a template of the form, otherwise use

If you wish to create a new category for this list, please propose it at WP:WSS/P first. Whenever new categories are added to this list, please also add them at WikiProject Stub sorting/Stub types.

title: Category:Politician stubs


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User:Femto/elements e6

yttrium

zirconium

niobium

molybdenum

technetium

ruthenium

rhodium

palladium

silver

cadmium

title: User:Femto/elements e6


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User talk:Parlod

Welcome
Hello Parlod, Welcome to Wikipedia. Thank you for joining us. I hope you like the place and decide to stay. Here are a few good links for newcomers: I hope you enjoy editing here and being a Wikipedian! By the way, you can sign your name on Talk and vote pages using three tildes, like this: MetsBot. Four tildes (MetsBot 19:13, 6 January 2007 (UTC)) produces your name and the current date. If you have any questions, see the help pages, add a question to the village pump or ask me on my Talk page.
 * How to edit a page
 * Editing tutorial
 * Picture tutorial
 * How to write a great article
 * Naming conventions
 * Manual of Style

You may also be interested in the Latter Day Saint movement project as we are attempting to organize our efforts to improve the quality of articles related to CJC and its progeny. By the way - Be bold if you see a change like the "and/or" just make it - most of us will work hard to not bite the newcomers. Trödel| talk 20:32, 16 Feb 2005 (UTC)

You may also want to visit List of articles about Mormonism and join the project at []. As always, please add new Mormonism-related articles to List of articles about Mormonism. Welcome again! -Visorstuff 21:03, 16 Feb 2005 (UTC)

Thanks :) -Parlod

title: User talk:Parlod


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User:Femto/elements e7

caesium

barium

thallium

lead

bismuth

polonium

astatine

radon

title: User:Femto/elements e7


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User:Femto/elements e8

lanthanum

cerium

praseodymium

neodymium

promethium

samarium

europium

gadolinium

terbium

dysprosium

holmium

erbium

thulium

ytterbium

title: User:Femto/elements e8


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User:Femto/elements e9

lutetium

hafnium

tantalum

tungsten

 * 182W
 * 26.50%
 * > 1 E21 y
 * α
 * colspan="2"| not observed
 * 183W
 * 14.3%
 * > 1 E21 y
 * α
 * colspan="2"| not observed
 * 184W
 * 30.64%
 * > 1 E21 y
 * α
 * colspan="2"| not observed
 * > 1 E21 y
 * α
 * colspan="2"| not observed

 * 186W
 * 28.43%
 * > 1 E21 y
 * α
 * colspan="2"| not observed
 * colspan="2"| not observed

rhenium

osmium

iridium

platinum

gold

mercury

title: User:Femto/elements e9


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Wikipedia:Articles for deletion/Freedom to Marry Coalition

 This page is an archive of the discussion about the proposed deletion of the article below. This page is no longer live. Further comments should be made on the article's talk page rather than here so that this page is preserved as an historic record. The result of the debate was keep. Joyous 21:08, Feb 26, 2005 (UTC)

Freedom to Marry Coalition
Article doesn't establish notability, instead it's a glorified list. Vote to delete. --Spinboy 20:36, 16 Feb 2005 (UTC)
 * Are you kidding? A quick Google search of the phrase turns up more than 8,000 hits, including local chapters in several states and Canada. I agree that the article could use some expansion, but the bar is not an article's current state, it's whether an article has the potential to be encyclopedic. A Nexis search shows mentions in 274 articles, including those published in the San Francisco Chronicle, Boston Globe, Associated Press, Dallas Morning News, Washington Post, Los Angeles Times and more. It's clearly a player in the debate if national reporters are turning to it as a resource. It deserves a place here, though it should be expanded. Katefan0 22:16, Feb 16, 2005 (UTC)
 * Expanded a small bit and cleaned up some of the overzealous alphabet soup wikification. Katefan0 22:30, Feb 16, 2005 (UTC)
 * Keep. Definitely notable: heavily involved in a major national controversy.   — Gwalla | Talk 00:13, 17 Feb 2005 (UTC)
 * Keep. Another bogus VFD. VFD is not cleanup. Bad content is not a justification for deletion.--Centauri 01:12, 17 Feb 2005 (UTC)
 * Keep in present form. Reasonably significant political entity as of 2005. Dpbsmith (talk) 02:20, 17 Feb 2005 (UTC)
 * Keep and expand, notable. Megan1967 05:51, 17 Feb 2005 (UTC)
 * Keep and expand. --Angr 07:16, 17 Feb 2005 (UTC)
 * Keep. Trilobite (Talk) 10:05, 17 Feb 2005 (UTC)
 * Keep - rather important US LG Rights organization - Skysmith 11:36, 17 Feb 2005 (UTC)
 * Keep. Jonathunder 05:23, 2005 Feb 19 (UTC)

This page is now preserved as an archive of the debate and, like some other VfD subpages, is no longer 'live'. Subsequent comments on the issue, the deletion, or the decision-making process should be placed on the relevant 'live' pages. Please do not edit this page.


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User:Femto/elements e10

francium

radium

lawrencium

rutherfordium

dubnium

seaborgium

bohrium

hassium

meitnerium

darmstadtium

roentgenium

ununbium

ununtrium

ununquadium

ununpentium

ununhexium

title: User:Femto/elements e10


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User:Femto/elements e11

actinium

thorium

protactinium

uranium

neptunium

plutonium

americium

curium

berkelium

californium

einsteinium

fermium

mendelevium

nobelium

title: User:Femto/elements e11


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Wikipedia:Articles for deletion/Alicia Selby

 This page is an archive of the discussion about the proposed deletion of the article below. This page is no longer live. Further comments should be made on the article's talk page rather than here so that this page is preserved as an historic record. The result of the debate was delete. Mackensen (talk) 05:56, 23 Feb 2005 (UTC)

Alicia Selby
The article stub reads, in total, "Alicia Selby was the winner of Miss Teen Tennessee 2003, and 1st runner-up in Miss Teen USA 2003." Does being the winner of a beauty pageant in US-STATENAME make that individual "notable enough" for inclusion? (No vote.) GRider\talk 20:38, 16 Feb 2005 (UTC) This page is now preserved as an archive of the debate and, like some other VfD subpages, is no longer 'live'. Subsequent comments on the issue, the deletion, or the decision-making process should be placed on the relevant 'live' pages. Please do not edit this page.
 * Comment: Alicia Selby gets 232 hits on google.
 * Many of those pages on Google just list the results of Miss Teen USA 2003, or news articles about the pageant, but nothing notable about Selby herself. Zzyzx11 00:16, 17 Feb 2005 (UTC)
 * Strong delete. Nearly every high school has a contest like that. Miss deserves a page, but not miss . Radiant! 22:22, Feb 16, 2005 (UTC)
 * Comment -- Currently, the Miss Teen Tennessee USA article is the second state beauty pageant listed in the Beauty pageants category (the other is Rhode Island). But the Miss Tennessee Teen article has all the winners wikified. Zzyzx11 00:11, 17 Feb 2005 (UTC)
 * Delete. Not notable. And someone will have to do something about Miss Teen USA and Miss Teen Tennessee, but let's wait to see how the consensus on this will come out. JoaoRicardo 07:14, 17 Feb 2005 (UTC)
 * Delete, not notable, even if she had won. -R. fiend 08:51, 17 Feb 2005 (UTC)
 * Keep, and expand. Megan1967 09:17, 17 Feb 2005 (UTC)
 * Delete, participation in a beauty pageant doesn't make a person notable. Wile E. Heresiarch 07:35, 18 Feb 2005 (UTC)
 * Keep (though it is borderline) - David Gerard 23:06, 20 Feb 2005 (UTC)


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User:Femto/elements e12

Removed duplicated page. D'oh!

title: User:Femto/elements e12


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Green terror

The green terror (Andinoacara rivulatus, syn. Aequidens rivulatus) is a colorful freshwater fish in the cichlid family. The fish originates from the Pacific side of South America from the Tumbes River in Peru to the Esmeraldas River in Ecuador. It is polymorphic and can have white or gold-orange edging to the tail and dorsal fins. It has historically been confused with two other species that always have narrow, clearly defined white edging, the more southerly distributed A. stalsbergi (often considered the "true" green terror) and the more northerly A. blombergi.

Species
Males of A. rivulatus grow larger than females and may reach lengths of up to 30 cm. Females are sexually mature at around 12 cm.

As the name implies, late juvenile-phase and adult-phase specimens can be very aggressive. This is not guaranteed, however, and peaceful individuals can be observed. Juveniles of A. rivulatus are often sold in aquarium stores. This species is not to be confused with the blue acara (A. pulcher) which is similar in appearance; the blue acara is not as aggressive and does not grow as large as the gold saum.

The fish is somewhat deep-bodied, possessing a prominent forehead. Adult males develop a pronounced forehead hump, composed of fatty tissue. Juveniles are tan colored with silver-blue flecks and lack the bright iridescent blue, green, and orange coloration and long, flowing fins of adult specimens.

In nature, A. rivulatus lives in a tropical climate and prefers water with a 6.5–8.0 pH, a water hardness of 25.0 dGH, and a temperature range of 20 –.

In the aquarium
A. rivulatus is a popular fish in the aquarium trade and is noted for its hardiness, as well as its aesthetic appeal. The fish may grow up to 30 cm (12 in) in length, though 15 to 20 cm (6– to 8 in) lengths are more typical, fish do not grow based on the size of their habitat.

A. rivulatus is an inherently aggressive fish. With the exception of fully mature P. dovii (wolf cichlid) and P. managuense (jaguar cichlid), which rarely cohabit peacefully, A. rivulatus is cunning enough to survive—and even thrive—with larger cichlids. However, as a rule, a preying cichlid is capable of consuming any animal whose dimensions are inferior to that of the cichlid's fully expanded jaw. A. rivulatus of an appropriate size can often be assimilated with R. octofasciata (Jack Dempsey cichlid), A. citrinellus (midas cichlid), T. meeki (firemouth cichlid), flowerhorns (no scientific designation), smaller P. managuense (jaquar cichlid)  and P. dovii (wolf cichlid), and other similarly aggressive cichlids.

Role in hormone research
A. rivulatus has also been used as a model for the effects of testosterone on lateralization.

title: Green terror