Anežka Kotěrová
email: koterova.a@seznam.cz

Vlastimil Králík

Rebeka Rmoutilová

Lukáš Friedl

Pavel Růžička

Jana Velemínská

François Marchal

Jaroslav Brůžek

Impact of 3D Surface Scanning Protocols on the Os Coxae Digital Data: Implications for Sex and Age-at-death Assessment

Keywords: Laser Scanning, Structured Light Technology, RedLux Profiler, Biological Profile, Age and Sex Estimation, Os Coxae

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The 3D imaging technologies, i.e. surface scanning (both, laser and structured light), as well as computed tomography (CT) scanners and micro-CT scanners became widely used in anatomical research over the last few years [START_REF] Villa | Evaluating osteological ageing from digital data[END_REF]. All of these devices enable to obtain 3D models of desired objects. Surface scanning contrary to CT scanning offers several practical advantages, for instance higher portability of most devices, texture capture options, low cost, and rapid post-processing. Furthermore, they can be operated without certification since there is no radiation involved during the scanning process [START_REF] Friess | Calvarial shape variation among Middle Pleistocene hominins: An application of surface scanning in paleoanthropology[END_REF][START_REF] Kuzminsky | Three-dimensional laser scanning : potential uses for museum conservation and scientific research[END_REF][START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF]. On the other hand, internal structures remain hidden [START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF]. Scanning technologies based on visible light have spread to many different disciplines: e.g. to forensic science [START_REF] Thompson | A new method for the recovery and evidential comparison of footwear impressions using 3D structured light scanning[END_REF][START_REF] Naether | Crime scene reconstruction using 3-D scanning and medical imaging technologies[END_REF], anthropology and paleoanthropology [START_REF] Friess | Calvarial shape variation among Middle Pleistocene hominins: An application of surface scanning in paleoanthropology[END_REF][START_REF] Kuzminsky | Three-dimensional laser scanning : potential uses for museum conservation and scientific research[END_REF][START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF][START_REF] Slizewski | Surface scanning of anthropological specimens: nominalactual comparison with low cost laser scanner and high end fringe light projection surface scanning systems[END_REF], anatomy and morphology [START_REF] Viggiano | A low-cost system to acquire 3D surface data from anatomical samples[END_REF][START_REF] Aung | Evaluation of the laser scanner as a surface measuring tool and its accuracy compared with direct facial anthropometric measurements[END_REF][START_REF] Cantín | Generation of 3D Tooth Models Based on Three-dimensional Scanning to Study the Morphology of Permanent Teeth[END_REF][START_REF] Gibelli | Threedimensional facial anatomy evaluation: Reliability of laser scanner consecutive scans procedure in comparison with stereophotogrammetry[END_REF], and paleontology and archaeology [START_REF] Viggiano | A low-cost system to acquire 3D surface data from anatomical samples[END_REF][START_REF] Adams | Surface model and tomographic archive of fossil primate and other mammal holotype and paratype specimens of the ditsong national museum of natural History, Pretoria, South Africa[END_REF][START_REF] Mcpherron | Structured light scanning for high-resolution documentation of in situ archaeological finds[END_REF]. They allowed for new applications and brought additional advantages. It is now possible, for instance, to create and archive digital copies of skeletal remains as 3D models in the virtual environment. Such digital osteological collections are invaluable for researchers for several reasons. First, they represent a possibility of permanent storage or documentation and conservation of bones, which are then accessible even though the real specimen is no longer available or does not exist anymore. Virtual storage can serve not only as a repository but also enables easier and faster sharing of data among researchers and institutions (e.g. [START_REF] Villa | Evaluating osteological ageing from digital data[END_REF][START_REF] Kuzminsky | Three-dimensional laser scanning : potential uses for museum conservation and scientific research[END_REF][START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF][START_REF] Viggiano | A low-cost system to acquire 3D surface data from anatomical samples[END_REF][START_REF] Adams | Surface model and tomographic archive of fossil primate and other mammal holotype and paratype specimens of the ditsong national museum of natural History, Pretoria, South Africa[END_REF][START_REF] Guydish | Using digitized Native American skeletal remains to conduct osteological analyses[END_REF][START_REF] Mathys | Comparing 3D digitizing technologies: What are the differences?[END_REF]). The reduced need for physical manipulation with skeletal material is certainly a great advantage [START_REF] Mathys | Comparing 3D digitizing technologies: What are the differences?[END_REF] since the preservation of dry bones varies as they are constantly being used for scientific purposes [START_REF] Ferreira | A reflection on the maintenance of identified skeletal collections state of preservation[END_REF]. Virtual models could be used for teaching and research, as well as for 3D printing which can be used for exhibition purposes [START_REF] Cantín | Generation of 3D Tooth Models Based on Three-dimensional Scanning to Study the Morphology of Permanent Teeth[END_REF][START_REF] Errickson | Towards a Best Practice for the Use of Active Non-contact Surface Scanning to Record Human Skeletal Remains from Archaeological Contexts[END_REF][START_REF] Klein | From scan to print: 3D printing as a means for replication[END_REF] or as demonstrative evidence in court [START_REF] Carew | A Preliminary Investigation into the Accuracy of 3D Modeling and 3D Printing in Forensic Anthropology Evidence Reconstruction[END_REF][START_REF] Chase | The Next Generation of Crime Tools and Challenges: 3D Printing[END_REF]. Last but not least, digital technologies enable us to virtually reconstruct damaged skeletal material, e.g. incompletely preserved fossil remains [START_REF] Rmoutilová | Virtual reconstruction of the Upper Palaeolithic skull from Zlatý Kůň, Czech Republic: Sex assessment and morphological affinity[END_REF][START_REF] Weber | Virtual anthropology: the digital evolution in anthropological sciences[END_REF][START_REF] Benazzi | Technical Note: Virtual reconstruction of KNM-ER 1813 Homo habilis cranium[END_REF].

Traditional methods of biological profile estimation which use morphometric or visual approach are usually used for defining the biological profile of an individual in bioarchaeology as well as in forensic anthropology (e.g. [START_REF] Langley-Shirley | Forensic Anthropology A Comprehensive Introduction[END_REF][START_REF] Nikita | Osteoarchaeology: A guide to the macroscopic study of human skeletal remains[END_REF]). However, virtual bone models have become of paramount importance and are commonly used for instance to measure metric variables in order to estimate sex [START_REF] Mestekova | A Test of the DSP Sexing Method on CT Images from a Modern French Sample[END_REF][START_REF] Chapman | Sex determination using the Probabilistic Sex Diagnosis (DSP : Diagnose Sexuelle Probabiliste) tool in a virtual environment[END_REF].

The consistency between dimensions taken directly from dry bones and from their virtual representations [START_REF] Chapman | Sex determination using the Probabilistic Sex Diagnosis (DSP : Diagnose Sexuelle Probabiliste) tool in a virtual environment[END_REF][START_REF] Citardi | Comparison of scientific calipers and computed-enabled CT review for the measurement of skull base and craniomaxillofacial dimensions[END_REF][START_REF] Corron | Evaluating the Consistency, Repeatability, and Reproducibility of Osteometric Data on Dry Bone Surfaces, Scanned Dry Bone Surfaces, and Scanned Bone Surfaces Obtained from Living Individuals[END_REF][START_REF] Verhoff | Digital forensic osteology-Possibilities in cooperation with the Virtopsy®project[END_REF] and even from their printed replicas [START_REF] Carew | A Preliminary Investigation into the Accuracy of 3D Modeling and 3D Printing in Forensic Anthropology Evidence Reconstruction[END_REF] has been proven repeatedly. Sex and stature estimates via linear measurements taken from virtual data obtained with two different laser scanners showed only a very small deviation from traditionally obtained data in one recent study [START_REF] Mullins | Estimating Biological Characteristics With Virtual Laser Data[END_REF]. Moreover, bones in virtual environment can undergo analyses that cannot be performed on dry bones, e.g. quantitative analyses of surface with the use of geometric morphometric.

For example, the geometric morphometric tools are used for sex estimation [START_REF] Musilová | Exocranial surfaces for sex assessment of the human cranium[END_REF][START_REF] Abdel Fatah | Improving sex estimation from crania using a novel three-dimensional quantitative method[END_REF][START_REF] Bulut | Sexual dimorphism in frontal bone roundness quantified by a novel 3D-based and landmark-free method[END_REF] and ancestry assessment [START_REF] Cavaignac | Three-dimensional geometric morphometric analysis reveals ethnic dimorphism in the shape of the femur[END_REF][START_REF] Murphy | A Morphometric Outline Analysis of Ancestry and Sex Differences in Cranial Shape[END_REF]. To estimate age-at-death, the pelvic articulations (the pubic symphysis, the auricular surface, and the acetabulum) are often used since their surfaces undergo changes with aging and recently, these surfaces have begun to be evaluated quantitatively [START_REF] Villa | Quantitative analysis of the morphological changes of the pubic symphyseal face and the auricular surface and implications for age at death estimation[END_REF][START_REF] Slice | Modeling Bone Surface Morphology: A Fully Quantitative Method for Age-at-Death Estimation Using the Pubic Symphysis[END_REF][START_REF] Stoyanova | An enhanced computational method for age-atdeath estimation based on the pubic symphysis using 3D laser scans and thin plate splines[END_REF][START_REF] Stoyanova | A Computational Framework for Ageat-Death Estimation from the Skeleton: Surface and Outline Analysis of 3D Laser Scans of the Adult Pubic Symphysis[END_REF][START_REF] San-Millán | Shape variability of the adult human acetabulum and acetabular fossa related to sex and age by geometric morphometrics. Implications for adult age estimation[END_REF]. Such analyses tend to be more objective than traditional visual methods based on scoring as they allow for the evaluation of morphological variation of the skeletal material independent of the human eye and experience of the researcher [START_REF] Errickson | Towards a Best Practice for the Use of Active Non-contact Surface Scanning to Record Human Skeletal Remains from Archaeological Contexts[END_REF].

Villa et al. [START_REF] Villa | Surface Curvature of Pelvic Joints from Three Laser Scanners: Separating Anatomy from Measurement Error[END_REF] raised an important question whether the results of surface quantification used for age estimation from different laser scanners are comparable. The precision and repeatability of measurements among different scanning devices are necessary in order to ensure the reliability of biological profile estimation methods. We extended this question to another parameter of biological profile -sex estimation (besides age estimation) and, in contrast to the original study where only laser scanners were used, both, laser and structured light, scanners were compared.

The aims of the present study are twofold. The first is to compare 3D models of os coxae made by two different surface scanners (NextEngine laser scanner and HP 3D Structured Light Scanner Pro S2) with a reference sample derived from RedLux Profiler device. Second, we aimed at assessing whether the scanning equipment has an effect on the age-at-death estimation (via quantitative method of

Material

The skeletal sample used in the present study originates from the cemetery of the 2 nd church in Mikulčice settlement (9th-10th century AD), south Moravia, Czech Republic. This sample represents a medieval population of central Europe that belonged to the Great Moravian Empire [START_REF] Poláček | Das Hinterland des frühmitelalterlichen Zentrums in Mikulčice. Stand und Perspektiven der Forschung[END_REF]. We used 18 adult individuals, of which 11 had well preserved ossa coxa on both sides and seven individuals who only had left or right bone well preserved. Altogether, 29 ossa coxa were used in the present study. Only individuals with well-preserved articular surfaces (pubic symphysis and auricular surface) were selected. Even if we do not know the real age and sex of individuals, we can compare the resulting estimates derived from surface models digitized with different scanning technologies (HP 3D SLS, NextEngine and RedLux) with each other.

Methods

3.1.Digitization of skeletal material

A small sample of pubic symphyses (n=5) was digitized with the use of RedLux Profiler contactless metrology device (RedLux Ltd., Southampton, UK) [START_REF] Nawabi | What Causes Unexplained Pain in Patients With Metal-on metal Hip Devices? A Retrieval, Histologic, and Imaging Analysis[END_REF][START_REF] Cook | Volumetric assessment of material loss from retrieved cemented metal hip replacement stems[END_REF]. Samples were selected to include both smooth and significantly wrinkled symphyses. The scanned surface of key areas was utilized as reference surface to compare the resolution and quality of scans obtained by commonly used scanners. The whole surface of os coxae (n=29) was then digitized with two different scanning devices: the NextEngine 3D scanner Ultra HD and HP 3D Structured Light Scanner PRO S2. The scanning process as well as post-processing procedures with all three scanners are described in the following sections.

RedLux

The pubic symphyses of five selected ossa coxa were digitized using the RedLux profiler. The RedLux profiler device is designed for very precise surface measurements using a confocal sensor. Due to limited space capacity because of technical arrangement of the measuring device, physical casts of these five pubic symphyses needed to be obtained. As a casting material, the two-component Addition Cure Molding Rubber, known under the trade designation MM242R [46], was used. This material shows negligible volumetric change (linear shrinkage of 0.09%) and excellent quality and accuracy of surface reconstruction. It is also necessary to ensure thorough venting, using a desiccator and the vacuum pump that any air bubbles could not affect the volumetric change. Venting was carried out for min. of 10 minutes at 150 mbar vacuum. The resulting symphyseal cast for one selected sample is shown in Fig. 1.

[Figure 1, insert here] The casts were scanned using a highly accurate RedLux profiler device. The instrument was equipped with two high-precision movable linear and two rotary axes. The rotary stages carry the sample and the linear stages carry the sensor. With this sensor, the lens error commonly known as chromatic aberration is used to measure the distance to an object. By combining the sensor signal with the knowledge of the exact position of all 4 stages, 3D representation of the surface can be created. The instrument has the capability to measure the entire surface in a single procedure. The accuracy of the resulting point cloud is given by the resolution of 2 linear axes, resolution of the two rotational axes and the resolution of the probe. The resolution of each linear axis is 100 nm, the resolution of each rotary axis is 10 arc second and the resolution of the probe is 20 nm. Detailed description of the RedLux profiler device can be found in [START_REF] Tuke | 3D linear and volumetric wear measurement on artificial hip joints-Validation of a new methodology[END_REF]. The arrangement of the measuring device is shown in Fig. 2.

[Figure 2, insert here] Since the measurement of a surface by RedLux profiler is a free-form surface measurement task, it is important to scan the surface with a sufficiently dense point cloud to capture the real scanned surface with all its irregularities. Throughout all of the performed measurements, the point cloud density was 540 individual points per one rotation of the measured sample in vertical plane and within every single rotation, the measured sample was shifted approximately by 0.1 mm in the horizontal plane. More than 120,000 points were obtained for each sample using RedLux profiler device. The resulting point cloud for the selected sample is shown in Fig. 3.

[Figure 3, insert here] As it is shown in the Fig. 3, the point coverage on the sample surface is irregular due to the used scanning method. For subsequent comparison with HP 3D Structured Light Scanner Pro S2 and NextEngine laser scanner, it was necessary to get a regular network of points. First, the co-ordinate data collected from the RedLux profiler were interpolated using Matlab's function scatteredInterpolant (The Mathworks, Inc.). This function is included in the basic Matlab package and performs interpolation on 3D set of points that have no structure among their relative locations (scattered data set).

Interpolation step Δ = 0.025 mm and 'natural' interpolation method was chosen for both directions and it specifies the density of the net. Detailed description of this function can be found in the software Matlab [48]. Mesh representation of the surface is shown in Fig. 3.

NextEngine and HP 3D Structured Light Scanner PRO S2

The whole sample of ossa coxa was digitized by two different surface scanners. The laser scanner NextEngine 3D scanner Ultra HD and HP 3D Structured Light Scanner PRO S2 (previously known as DAVID SLS 2) with maximum resolution of 0.1 mm and 0.05 mm, respectively, were utilized. These two scanners represent different scanning technologies: laser and structured light technology. Since the aim of the present study is to compare the resulting 3D models from different surface scanners in terms of their surface representation and their possible impact on sex and age estimation, 3D models were created under the optimal conditions for the particular scanner.

The entire surface of os coxae was scanned to facilitate creation of the osteological collection, however only partial surfaces were used for some of the analyses in this study. The scanning process, as well as the post-processing (aligning multiple scans of each bone together to form a final polygonal mesh) was performed in the integrated software of each scanner (ScanStudio HD and David LaserScanner v.3.10.4, respectively). In the case of HP 3D Structured Light Scanner PRO S2 (hereinafter HP 3D SLS) the calibration was done with the 120 mm pattern that is ideal for objects of similar size to an os coxae. The scanning procedure was performed against black background and the scanned ossa coxa were manually rotated from both, the ventral and dorsal sides to create a solid model. In the case of NextEngine scanner, a dedicated rotational device was used to fix the bone and rotate it in front of the scanner so that each individual scan overlaps with the previous and the next one. Final scan was automatically assembled from these individual scans in the ScanStudio HD software. Each bone was attached to the device in two points, one along the iliac crest and one just in front of the ischial tuberosity. Both points were selected to minimize the areas where the laser beam does not reach and thus, to ensure that if there is a fraction of the surface to be reconstructed, such area is very small. These contact areas with the rotational device might have had approximately 1mm 2 each.

The final 3D models were saved in the stl format. Each mesh had to be simplified (consistent simplification on 3 millions of faces) in order to facilitate the manipulation. Subsequently, 3D models from the NextEngine 3D scanner had to be scaled (in MeshLab software [START_REF] Cignoni | MeshLab: an Open-Source Mesh Processing Tool[END_REF]) to obtain the same dimensions as the models from HP 3D SLS. The models of whole ossa coxa were used for sex estimation while the articular surfaces of pubic symphysis that we isolated from the rest of the bone (in MeshLab software) served as input data for age-at-death estimation.

3.2.Comparison of resulting 3D surfaces

Once the mesh representation of the pubic symphyseal surface was created, it became important to calculate the distribution of deviations between the actual and reference surfaces. Both surfaces were overlapped using the local best fit feature implemented in the commercially available software GOM Inspect [START_REF] Gom Gmbh | GOM Inspect[END_REF]. The function minimizes differences (square error) through all of the used points and the software tries to align the point sets so the differences are zero or as close to zero as possible. Resulting deviations were assessed with descriptive statistics (mean, standard deviation, median, and interquartile range) for all samples.

3.3.Biological profile assessment

Sex estimation

Linear dimensions derived from 3D surface models taken with two different scanners and dimensions taken directly from dry ossa coxa served as the input data for the DSP2 (Diagnose Sexuelle Probabiliste 2) sex determination method [START_REF] Murail | DSP: A tool for probabilistic sex diagnosis using worldwide variability in hip-bone measurements[END_REF][START_REF] Brůžek | Validation and reliability of the sex estimation of the human os coxae using freely available DSP2 software for bioarchaeology and forensic anthropology[END_REF]. The DSP2 method is based on linear discriminant analysis and posterior probabilities. The output of this sex determination tool is the probability of being male or female (sex is determined if the posterior probability is ≥ 0.95). Ten variables (or the number that could be measured for a given bone depending on its preservation, but at least four)

were measured according to their definitions (Table 1) described in the software interface. The variables were first measured on dry bones with appropriate measuring instruments (sliding caliper, friction divider, and pelvimeter). Second, measurements on 3D models were carried out in the Morphome3cs software [START_REF]Morphome3cs II[END_REF] in the virtual environment. Additionally, ten randomly chosen ossa coxa were measured twice over a period of four weeks (for all three types of acquisitions) for intraobserver error. All the measurements were taken by two researchers (AK and RR) both trained in pelvic osteometry with approximately the same experience. Apart from the intra-observer error calculated for linear variables, the inter-observer error between two equally trained observers was calculated as well.

[Table 1, insert here] To verify the accuracy of measured dimensions within each type of data taken by one observer (intra-observer error) the technical error of measurement (TEM) and the relative TEM (rTEM) expressed as a percentage were calculated. Both statistical characteristics are commonly used to evaluate intra-observer precision [START_REF] Mestekova | A Test of the DSP Sexing Method on CT Images from a Modern French Sample[END_REF][START_REF] Corron | Evaluating the Consistency, Repeatability, and Reproducibility of Osteometric Data on Dry Bone Surfaces, Scanned Dry Bone Surfaces, and Scanned Bone Surfaces Obtained from Living Individuals[END_REF][START_REF] Lottering | Morphometric modelling of ageing in the human pubic symphysis: Sexual dimorphism in an Australian population[END_REF][START_REF] Stomfai | Intra-and inter-observer reliability in anthropometric measurements in children[END_REF][START_REF] Machado | Application and validation of Diagnose Sexuelle Probabiliste V2 tool in a miscegenated population[END_REF]. The inter-observer error was computed for measurements taken on dry bones and on 3D models acquired with both scanners. A paired t-test was applied to assess whether significant differences between the two observers exist.

Age-at-death estimation

Age-at-death of our sample was estimated with the use of a recent computational method of Stoyanova et al. [START_REF] Stoyanova | An enhanced computational method for age-atdeath estimation based on the pubic symphysis using 3D laser scans and thin plate splines[END_REF][START_REF] Stoyanova | A Computational Framework for Ageat-Death Estimation from the Skeleton: Surface and Outline Analysis of 3D Laser Scans of the Adult Pubic Symphysis[END_REF]. This fully quantitative evaluation of pubic symphyseal surfaces is based on mathematical evaluation of the flatness of the surface, the curvature of ventral margin of pubic symphysis and their combination. The flatness of the surface captures the TPS/BE (Thin Plate Spline/Bending Energy) analysis and SAH-Score (described in detail in [START_REF] Slice | Modeling Bone Surface Morphology: A Fully Quantitative Method for Age-at-Death Estimation Using the Pubic Symphysis[END_REF]). The last computational method captures the curvature of ventral margin (VC) of pubic symphysis. These analyses are incorporated into the "forAge" software. Detailed description can be found in the original study [START_REF] Stoyanova | A Computational Framework for Ageat-Death Estimation from the Skeleton: Surface and Outline Analysis of 3D Laser Scans of the Adult Pubic Symphysis[END_REF].

The isolated articular surfaces of the pubic symphyses were simplified to 15,000 faces and saved in the ply format before loading to the forAge software to estimate the age-at-death. All the isolated pubic symphyseal surfaces (n=29) from the NextEngine, the HP 3D SLS, and the small sample (n=5) from RedLux were subjected to this quantitative analysis. The 3D models digitized with HP 3D SLS and NextEngine were compared using paired t-test. The small sample (n = 5) of pubic bones digitized with RedLux was also compared with the other samples. Differences within corresponding individuals between RedLux and HP 3D SLS and NextEngine respectively were evaluated with a paired onesample t-test.

Results

4.1.Comparison of resulting 3D surfaces

Examples of three isolated pubic symphyseal surfaces digitized with the three scanning devices are shown in Fig. 4.

[Figure 4, insert here] Visualization has been made in the form of color-coded maps of scatters of deviations between the reference and compared scans. This comparison was performed for all 5 selected specimens, both for HP 3D Structured Light Scanner Pro S2 and NextEngine laser scanner. The color scale bar on the map of deviations shows both positive and negative dimensional changes. The positive scale (red color) means that the compared surface is above the reference one and the negative scale (blue color) indicate that the compared surface is below the reference one. Gray color refers to the surface where no data were available from compared scanners (HP 3D SLS and NextEngine).

Example of color-coded maps for selected specimens are shown in Fig. 5. It can be seen that the largest and the smallest deviations from the reference surface are in the area of depressions or protrusions. This is more evident on specimens with more rugged surface (e.g. specimen no. 390). It shows that scans acquired by HP 3D SLS and NextEngine are smoothed out and do not capture as much detail as the reference surface obtained with RedLux profiler. This is due to the resolution of a particular scanning technology.

[Figure 5, insert here] Descriptive statistics of the resulting deviations are summarized in Table 2. The graphical comparison of the examined scanners is in Fig. 6 for all samples in the form of box plots. The deviations from the reference surface were slightly larger for the NextEngine than for the HP 3D SLS. In the case of NextEngine scanner, the interquartile range is higher for all samples and the standard deviation is higher as well, except for sample no. 383. Contrary to the NextEngine, there are more places where no data are available in scans from the HP 3D SLS, particularly in major depressions. From Table 2 and Fig. 6, it can also be seen that the value of the percentage difference of IQR is related to the degree of wrinkling of the surface. The percentage difference of IQR is significantly higher in samples which are more wrinkled, such as the sample no. 390 where a relatively large lateral wrinkling is evident (Fig. 5).

[Figure 6, insert here] [Table 2, insert here]

4.2.Sex estimation

For manual DSP method as well as for virtual DSP performed on both 3D models (HP 3D SLS and NextEngine) comparisons of estimated sexes (and agreement) between two observers were performed. The results of agreement in estimating male, female, and undetermined sex (N/A) of two researchers are shown in Tables 3 to 5. Some differences between observers were found, however opposite sex was never assigned to the same individual (rather, such individual was assigned as N/A).

[Tables 3 to 5, insert here]

Intra and inter-observer error of measurements

Results of intra-observer error are presented in Table 6. With measurements on dry bones, average TEM values were 0.80 mm and 0.62 mm respectively. TEM values for dry bones ranged between 0.32 and 1.35 mm with rTEM 0.35% -3.09% for the first researcher and for the second researcher from 0.45 to 1.01 mm, rTEM 0.20% -2.07%. The lowest average TEM values were reached by both researchers coincidently in the case of HP 3D SLS (0.62 and 0.56 mm respectively). The intra-observer error of the researcher 1 ranged between 0.35 and 1.24 mm for all ten dimensions and the rTEM ranged between 0.21% and 2.85%, and for researcher 2 between 0.24 and 0.88 mm with rTEM from 0.19% to 2.22%. The average intra-observer error for NextEngine was very similar to previous results (0.72 mm and 0.57 mm respectively). The error for the first researcher was between 0.26 and 1.38 mm, rTEM 0.20% -2.36% and TEM of researcher 2 ranged from 0.12 to 1.19 mm, with rTEM of 0.07% -1.95%.

[Table 6, insert here] The results of inter-observer error for variables measured directly on dry bones, on 3D models digitized with HP 3D SLS, and NextEngine respectively, are shown in Table 7. Significant differences between the two researchers when dry bones were measured have been found for IIMT, ISMM, SCOX, SS, and VEAC). DSP variables measured on 3D models from HP 3D SLS scanner were statistically different between the two researchers in IIMT, ISMM, SCOX, SS, SIS, and VEAC. Interobserver differences were also found for 3D measurements derived from NextEngine, namely for PUM, DCOX, SS, and VEAC.

[Table 7, insert here]

4.3.Age estimation

A paired t-test was applied to reveal potential differences between estimated ages derived from the 3D models of the isolated pubic symphyses from the NextEngine and the HP 3D SLS. The results are provided in Table 8. Differences in estimated ages were not statistically significant. The only exception was the TPS/BE regression model. Thus, the null hypothesis was not rejected for any of the models, except for TPS/BE model. These results can be interpreted as a negligible effect of scanning technologies on age estimation in this study. The sample of isolated pubic symphyseal surfaces derived from the RedLux Profiler (n=5) was also subjected to the age-at-death analysis. We did detect differences between HP 3D SLS and RedLux for SAH model, and between NextEngine and Redlux in SAH and SAH+VC model. The null hypothesis was not rejected in all other cases (see Table 9).

[Tables 8 and9, insert here]

Discussion

Scanning -problem of compatibility

Many researchers travel around the world and study osteological collections to design new methods or to validate already existing ones. Scanning technologies are already used in biological and forensic anthropology and became of paramount importance (e.g. [START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF][START_REF] Slizewski | Surface scanning of anthropological specimens: nominalactual comparison with low cost laser scanner and high end fringe light projection surface scanning systems[END_REF][START_REF] Carew | Imaging in forensic science : Five years on[END_REF]). One of the greatest benefits is a digital storage and sharing of osteological material. The material can come to researchers or it can be publicly available under certain conditions. However, research institutions are equipped with different scanning devices that may produce varying outputs. Therefore, it is necessary to verify that the outputs from different devices are comparable to each other (and under what settings) and that they do not affect further analyses such as the estimation of the biological profile. The comparability of outputs from different devices is especially important in age-at-death estimation since minor changes on joint surface may influence the final estimate significantly. However, the consistency of outputs from different devices for biological profile estimation needs to be tested. This has not yet been sufficiently explored, except for a few exceptions, such as the aforementioned study of Villa et al. [START_REF] Villa | Surface Curvature of Pelvic Joints from Three Laser Scanners: Separating Anatomy from Measurement Error[END_REF].

To test the effect of scanning device on forensic methods of biological profile estimation we chose the sex and age-at-death estimation. For sexing, the method called DSP is well established, and its reliability has been proven and guarantees objectivity (e.g. [START_REF] Mestekova | A Test of the DSP Sexing Method on CT Images from a Modern French Sample[END_REF][START_REF] Chapman | Sex determination using the Probabilistic Sex Diagnosis (DSP : Diagnose Sexuelle Probabiliste) tool in a virtual environment[END_REF][START_REF] Quatrehomme | Sex determination using the DSP (probabilistic sex diagnosis) method on the coxal bone: Efficiency of method according to number of available variables[END_REF][START_REF] Paz | Validity of the probabilistic sex diagnosis method (DSP) on 3D CT-scans from modern Danish population[END_REF]). The forAge software is one of the few age estimation approaches using mathematical quantification of bone surface that has been published so far. Repeatability of the method was tested with excellent results [START_REF] Kim | Testing Reliability of the Computational Age-At-Death Estimation Methods between Five Observers Using Three-Dimensional Image Data of the Pubic Symphysis[END_REF] and its use is suitable for individuals under 40 years of age [START_REF] Kotěrová | A validation study of the Stoyanova et al. method (2017) for age-at-death estimation quantifying the 3D pubic symphyseal surface of adult males of European populations[END_REF].

The advantages and disadvantages of both scanners (NextEngine and HP 3D Structured Light Scanner PRO S2) and the technologies represented by them should be discussed to help future users assess them for their research goals. Both scanning technologies are used on skeletal material. However, laser scanning technology is generally preferred among anthropologists [START_REF] Errickson | Towards a Best Practice for the Use of Active Non-contact Surface Scanning to Record Human Skeletal Remains from Archaeological Contexts[END_REF]. One of the most often used scanners is the NextEngine laser scanner (e.g. [START_REF] Slizewski | Surface scanning of anthropological specimens: nominalactual comparison with low cost laser scanner and high end fringe light projection surface scanning systems[END_REF][START_REF] Cantín | Generation of 3D Tooth Models Based on Three-dimensional Scanning to Study the Morphology of Permanent Teeth[END_REF][START_REF] Slice | Modeling Bone Surface Morphology: A Fully Quantitative Method for Age-at-Death Estimation Using the Pubic Symphysis[END_REF][START_REF] Algee-Hewitt | The reality of virtual anthropology: Comparing digitizer and laser scan data collection methods for the quantitative assessment of the cranium[END_REF][START_REF] Gualdi-Russo | Giovanni Battista Morgagni: facial reconstruction by virtual anthropology[END_REF][START_REF] Shearer | Sexual dimorphism in human browridge volume measured from 3D models of dry crania: A new digital morphometrics approach[END_REF]) which was also used for a development of one method of age estimation that we tested in our study [START_REF] Stoyanova | An enhanced computational method for age-atdeath estimation based on the pubic symphysis using 3D laser scans and thin plate splines[END_REF][START_REF] Stoyanova | A Computational Framework for Ageat-Death Estimation from the Skeleton: Surface and Outline Analysis of 3D Laser Scans of the Adult Pubic Symphysis[END_REF]. Although less attention has been paid to HP 3D SLS, its use is now on the rise. HP 3D SLS was used or tested in many studies on human or animal bones, as well as for other purposes [START_REF] Klein | From scan to print: 3D printing as a means for replication[END_REF][START_REF] Tambusso | Description of the stylohyal bone of a giant sloth (Lestodon armatus)[END_REF][START_REF] Maté-González | Statistical Comparison between Low-Cost Methods for 3D Characterization of Cut-Marks on Bones[END_REF][START_REF] Edwards | The Accuracy and Applicability of 3D Modeling and Printing Blunt Force Cranial Injuries[END_REF][START_REF] Viciano | Scheuermann's disease in a juvenile male from the late Roman necropolis of Torrenueva (3rd-4th century CE, Granada, Spain)[END_REF][START_REF] Porter | A Simple Photogrammetry Rig for the Reliable Creation of 3D Artifact Models in the Field: Lithic Examples from the Early Upper Paleolithic Sequence of Les Cottés (France)[END_REF][START_REF] Massinon | Study of retention variability on an early growth stage herbaceous plant using a 3D virtual spraying model[END_REF][START_REF] Secher | Accuracy and reproducibility of the DAVID SLS-2 scanner in three-dimensional facial imaging[END_REF][START_REF] Yravedra | Differentiating percussion pits and carnivore tooth pits using 3D reconstructions and geometric morphometrics[END_REF]. Both tested scanners are commercially available, and they belong to the low-cost category of scanners (under $ 3,000), which makes them accessible to a great number of users. Moreover, the cost vs. performance ratio very often makes them the first choice. In terms of time needed to acquire and post-process scans, HP 3D SLS outperforms NextEngine scanner, mainly because the laser technology is more time consuming than other technologies [START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF][START_REF] Mathys | Comparing 3D digitizing technologies: What are the differences?[END_REF]. Both scanners are characterized by high portability, and can be easily carried to various collections around the world. Also, both scanners are able to capture texture and provide 3D textured mesh as an output which is often a great benefit [START_REF] Slizewski | Surface scanning of anthropological specimens: nominalactual comparison with low cost laser scanner and high end fringe light projection surface scanning systems[END_REF].

NextEngine as well as HP 3D SLS have, nonetheless, some limitations. Although laser and white light scanners are commonly used on skeletal material, they are limited in scanning dark and transparent objects or objects reflecting light (e.g. tooth enamel) [START_REF] Slizewski | Surface scanning of anthropological specimens: nominalactual comparison with low cost laser scanner and high end fringe light projection surface scanning systems[END_REF][START_REF] Zaimovic-Uzunovic | Influences of Surface Parameters On Laser 3D Scanning[END_REF]. Systems using blue light (e.g. HP 3D SLS) should reduce this limitation [START_REF] Slizewski | Surface scanning of anthropological specimens: nominalactual comparison with low cost laser scanner and high end fringe light projection surface scanning systems[END_REF], however one of the authors (A.K.) who routinely operates with the HP 3D SLS has experienced similar issues (problems to capture very dark areas on objects). In case of HP 3D SLS, constant ambient light during scanning is recommended and a generally darker room is better suited for scanning.

It has to be emphasized that the quality of 3D models could be affected by post-processing. The simplification is often required to reduce file size, fasten data processing and manipulation with virtual data [START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF][START_REF] Veneziano | Surface smoothing, decimation, and their effects on 3D biological specimens[END_REF]. Even though in case of smoothing procedures, the positive or negative effect on mesh topology depends on algorithm used, decimation procedure always leads to loss of information, which affects mesh topology and measurements. With increasing the mesh triangle size, the accuracy of measurements is negatively affected [START_REF] Veneziano | Surface smoothing, decimation, and their effects on 3D biological specimens[END_REF][START_REF] Sholts | Variation in the measurement of cranial volume and surface area using 3d laser scanning technology[END_REF]. The amount of decimation should be defined with regards to the type of objects and the purpose for which it will be used [START_REF] Friess | Scratching the surface? The use of surface scanning in physical and paleoanthropology[END_REF][START_REF] Veneziano | Surface smoothing, decimation, and their effects on 3D biological specimens[END_REF]. In the present paper, the only post-processing step performed was decimation, in order to enable for a better manipulation since the whole ossa coxa were scanned. Simplification was applied to all models equally to enhance manipulation with minimum information lost. Other post-processing procedures (e.g hole filling, smoothing) were not performed to prevent the quality of the scan being negatively affected. Therefore, the dimensions in our study could not be affected by different postprocessing procedures. However, it would be of great benefit for future studies if somebody tested how much simplified can the surface be without loosing too much information and getting inaccurate or imprecise estimates.

In our study, we worked with the assumption that different scanner operators or different scanning protocols do not affect the resulting 3D models and their geometric properties [START_REF] Sholts | Variation in the measurement of cranial volume and surface area using 3d laser scanning technology[END_REF]. However, all the virtual data was acquired by experienced operators to ensure the consistency of scanned data. All the data was taken under the optimal conditions of each scanner. Since it is complicated to compare scanning devices among themselves, such an approach seemed the most reasonable to us.

Moreover, it was also used in the study by Villa et al. [START_REF] Villa | Surface Curvature of Pelvic Joints from Three Laser Scanners: Separating Anatomy from Measurement Error[END_REF].

Comparison of tested devices

Even the visual inspection of final 3D models revealed that Redlux scanner captures much more detail than the other two low-cost scanners.

Our results indicate that the NextEngine laser scanner has captured fewer details (it smoothed the surface the most and the loss of information was the largest) than HP 3D Structured Light Scanner when compared to reference scans from Redlux Profiler. Singh et al. [START_REF] Singh | Comparison of laser and structured light scanning techniques for neurosurgery applications[END_REF] reported that structured light technology is more accurate in comparison to laser technology when used to calculate surface area and volume. Results of another study [START_REF] Mathys | Comparing 3D digitizing technologies: What are the differences?[END_REF] indicate that structured light technology captured external structures better than other technologies (including laser technology). It should be noted here that these differences, as found in our study, are relatively small and their impact on further analyses (biological profile analyses) will be negligible. Our analyses of sex and age estimation confirm this notion.

Influence of different scanning device on the linear measurements and sex assessment

Both observers estimated sex of all individuals almost uniformly independently on the scanning technology. The intraobserver and interobserver errors of linear measurements were evaluated.

According to Camison et al. [START_REF] Camison | Validation of the Vectra H1 portable three-dimensional photogrammetry system for facial imaging[END_REF] who divided rTEM values into five categories: <1% = excellent, 1-3.9% = very good, 4-6.9% = good, 7-9.9% = moderate, and >10% = poor, intraobserver error of both researchers in most cases fell into "excellent" category and the rest into "very good" category (the highest value reached 3.09%). Even though, our results of inter-observer error show some statistically significant differences between two observers, which may not be due to different scanners, they have never led to the opposite classification. Here, our results could be interpreted in a similar way as in the study of Mullins and Albanesse [START_REF] Mullins | Estimating Biological Characteristics With Virtual Laser Data[END_REF], that even significant differences may not affect estimates and utility of this type of data. To our best knowledge, this is the first study testing the DSP method on virtual models acquired by surface scanners. Previously, the method was tested only on 3D models acquired with the CT scanning (e.g. [START_REF] Mestekova | A Test of the DSP Sexing Method on CT Images from a Modern French Sample[END_REF][START_REF] Chapman | Sex determination using the Probabilistic Sex Diagnosis (DSP : Diagnose Sexuelle Probabiliste) tool in a virtual environment[END_REF][START_REF] Paz | Validity of the probabilistic sex diagnosis method (DSP) on 3D CT-scans from modern Danish population[END_REF]).

Effect of scanning device on delicate surface changes in the age-at-death estimation

Although the comparison of captured surfaces from both low-cost scanners showed slightly better outputs for the HP 3D SLS, almost no effect on quantitative methods of age estimation was detected (the only exception was TPS/BE model). We can conclude that other scanning technologies (at least structured light, here represented by the HP 3D SLS) can be used to estimate age using the method of Stoyanova et al. [START_REF] Stoyanova | An enhanced computational method for age-atdeath estimation based on the pubic symphysis using 3D laser scans and thin plate splines[END_REF][START_REF] Stoyanova | A Computational Framework for Ageat-Death Estimation from the Skeleton: Surface and Outline Analysis of 3D Laser Scans of the Adult Pubic Symphysis[END_REF].

When compared to the outputs obtained with the Redlux, significant differences in one out of five models were found in case of HP 3D SLS and two out of five for the NextEngine. However, it was not assumed in our study that Redlux should be used to estimate age-at-death by quantitative methods. This device was selected only as a reference for comparing two low cost scanners and its routine use in anthropology would be too expensive and impractical.

Larger deviations between captured surfaces are more evident on more wrinkled surfaces, e.g. sample no. 390 that was obviously a very young individual (partially unfused iliac crest). Therefore, further investigation is needed to clarify whether the effect of different scanners is more obvious in more wrinkled surfaces, typical for younger individuals.

Conclusion

The two tested scanning technologies, structured light and laser represented by HP 3D SLS and NextEngine, respectively did show only small surface deviation from the reference sample in our study. The structured light technology seems to be more accurate (captures slightly more details) than laser technology. Nevertheless, no significant impact on age and sex estimates was observed.

Except for TPS/BE model in age estimation. Thus, it appears that the type of scanner does not have a significant effect on the estimate of the biological profile under optimal scanning settings. However, we encourage further investigation especially in case of age-at-death estimation, where even subtle changes to the articulation surface are evaluated. Both, existing as well as future analyses of these surfaces could be sensitive to their different 3D representations acquired with different devices.

Fig. 1 Fig. 2 Fig. 4 Fig. 5 Fig. 6

 12456 Fig. 1 Symphyseal cast of sample no. 390

  Stoyanova et al., 2017) and sex assessment (via DSP2 -Diagnose Sexuelle Probabiliste of Brůžek et al.,

	2017).

Table 1 . Os coxae measurements in DSP2.

 1

		Variable	Name	Reference
	1	PUM	Acetabulo-symphyseal pubic length	(Novotný 1986)
	2	SPU	Cotylo-pubic width	(Gaillard 1960)
	3	DCOX (M1) Coxal length	(Braüer 1988)
	4	IIMT (M15.1) Greater sciatic notch height	(Braüer 1988)
	5	ISMM	Ischium post-acetabular length	(Schulter-Ellis et al. 1983)
	6	SCOX (12)	Iliac or coxal breadth	(Braüer 1988)
	7	SS	Spino-sciatic length	(Gaillard 1960)
	8	SA	Spino-auricular length	(Gaillard 1960)
	9	SIS (M14.1) Cotylo-sciatic breadth	(Braüer 1988)
	10 VEAC (M22) Vertical acetabular diameter	(Braüer 1988)

Table 2 . Results of quantitative comparison between measured samples (HP 3D SLS vs. Reference sample* and NextEngine vs. Reference sample).

 2

	Specimen	Scanner	Mean value for normal distribution	Median	Standard deviation	Interquartile span	Percentage difference IQR
	352	HP 3D NextE	26 26	2 4	154 204	147 220	+50
	359	HP 3D NextE	8 7	-8 -4	118 146	104 140	+35
	382	HP 3D NextE	14 13	6 0	113 138	114 141	+24
	383	HP 3D NextE	15 26	5 15	187 174	122 184	+51
	390	HP 3D NextE	14 21	0 2	126 202	88 212	+141

*Reference samples were acquired by the RedLux Profiler.

Table 3 . Agreement between researchers in estimating sex with the use of DSP on dry bones. Shaded cells correspond to the number of individuals in which the two researchers agreed.

 3

				Researcher 1
			Male	Female N/A Total
		Male	15	0	0	15
	Researcher	Female	0	13	0	13
	2	N/A	1	0	0	1
		Total	16	13	0	29

Table 4 . Agreement between researchers in estimating sex with the use of the DSP on virtual models from HP 3D SLS. Shaded cells correspond to the number of individuals in which the two researchers agreed.

 4

				Researcher 1
			Male Female N/A Total
		Male	15	1	0	16
	Researcher	Female	0	11	0	11
	2	N/A	0	1	1	2
		Total	15	13	1	29

Table 5 . Agreement between two researchers in estimating sex with the use of the DSP on virtual models from NextEngine. Shaded cells correspond to the number of individuals in which the two researchers agreed. Researcher 1 Male Female N/A Total Researcher 2

 5

	Male	15	0	0	15
	Female	0	12	0	12
	N/A	1	0	1	2
	Total	16	12	1	29

Table 6 . The intra-observer precision of measured variables used for DSP.

 6 = technical error of measurement, rTEM = relative technical error of measurement, the measurement unit of TEM is given in mm, rTEM expressed as a percentage

				Researcher 1					Researcher 2
		dry bones	HP 3D SLS	NextEngine	dry bones	HP 3D SLS	NextEngine
	Variable	TEM rTEM TEM rTEM TEM rTEM TEM rTEM TEM	rTEM TEM	rTEM
	PUM	1.19	1.58 1.24	1.64 1.38	1.84 0.56	0.74	0.82	1.07	1.17	1.51
	SPU	0.89	3.09 0.80	2.85 0.67	2.36 0.56	1.97	0.63	2.22	0.46	1.57
	DCOX	0.77	0.35 0.49	0.23 1.11	0.51 0.45	0.20	0.83	0.38	1.19	0.55
	IIMT	1.35	2.91 0.91	2.05 0.78	1.76 0.94	2.07	0.88	2.02	0.85	1.95
	ISMM	1.25	1.14 0.57	0.52 1.31	1.16 0.47	0.43	0.67	0.60	0.55	0.49
	SCOX	0.57	0.35 0.35	0.21 0.33	0.20 0.55	0.34	0.30	0.19	0.12	0.07
	SS	0.43	0.57 0.43	0.58 0.26	0.35 0.60	0.81	0.24	0.33	0.47	0.63
	SA	0.68	0.84 0.53	0.67 0.40	0.50 1.01	1.25	0.38	0.48	0.42	0.52
	SIS	0.32	0.80 0.40	1.03 0.38	0.97 0.49	1.24	0.43	1.11	0.26	0.66
	VEAC	0.54	0.95 0.52	0.91 0.58	1.00 0.60	1.07	0.45	0.81	0.24	0.43
	mean	0.80		0.62		0.72		0.62		0.56	0.57

TEM

Thin plate spline/Bending energy,

Slice and Algee-Hewitt score,

Ventral curvature,

combination of TPS/BE and VC (multivariate model),

combination of SAH score and VC (multivariate model) P-values are shown for a two-tailed test.

Acknowledgments

We would like to thank the Department of Anthropology at the National Museum in Prague for providing the osteological material. This research has been supported by the research grant Charles University Grant Agency No. 642218 and partially by IRN Bipedal Equilibrium, CNRS, France.


PhD Louise Corron
email: lcorron@unr.edu

PhD Pascal Adalian
email: pascal.adalian@univ-amu.fr

PhD Silvana Condemi
email: silvana.condemi@univ-amu.fr

PhD François Marchal
email: francois.marchal@univ-amu.fr

MSc David Navega
email: dsnavega@gmail.com

Sub-adult Aging Method Selection (SAMS): a decisional tool for selecting and evaluating sub-adult age estimation methods based on standardized methodological parameters

Keywords: subadult, age estimation, decisional tool, SAMS, Forensic Anthropology Population Data

Objectives: A decisional tool was developed to select sub-adult age estimation methods referenced in a centralized database. Through a freely accessible webpage interface, this tool allows users to evaluate how much the sampling and statistical protocols of these referenced methods comply with methodological recommendations published for building and applying methods in forensic anthropology. Materials and methods: 261 publications on sub-adult age estimation were collected. Three search parameters describing the anatomical element(s) and the indicators used to obtain age estimates are chosen by the user to filter the database and present the publications that best correspond to the user's selection. A simple algorithm was created to score age estimation methods according to their relevance and validity. "Relevance" and "Validity" parameters indicate how much a publication complies with user queries and published methodological recommendations, respectively; "Score" is a combination of "Relevance" and "Validity". The closer these parameters are to 1, the better the method complies with the user's choice and standardized protocols. Results: The publications resulting from the user's query appear as search results alphabetically. They are characterized by their "Relevance", "Validity" and "Score" values and descriptors relating to their methodology, sampling and statistical protocols. The reference of the publications and an URL to access them online are also provided. Conclusions: SAMS is a decisional tool based on a centralized database for selecting, accessing and evaluating sub-adult age estimation methods based on published methodological recommendations. Protocol validity can be easily and fully accessed to provide the necessary information for method evaluation. The database will be gradually updated and implemented as new sub-adult age estimation methods are made available online.

INTRODUCTION

Bioanthropologists, bioarcheologists, forensic practitioners, or radiologists can choose from a variety of methods to estimate the skeletal or dental age of living or deceased juvenile or sub-adult individuals. Even though most of these methods are published in scientific journals and/or well-known osteology or bioanthropology books, different levels of personal, institutional, or even national propensity, on top of the significant number of available methods, result in the routine application of only a few of these methods in anthropological practice [START_REF] Corron | A critical review of sub-adult age estimation in biological anthropology: do methods comply with published recommendations?[END_REF]. As a result, although hundreds of methods are available for subadult age estimation, many remain unknown or less known in the bioanthropological contexts they are primarily applied in, e.g. the forensic and bioarcheological contexts. In addition, the medical community, namely practitioners who monitor children's skeletal and dental growth and development, could also benefit from an expanded dissemination of these methods. Therefore, the need for subadult age estimation methods to be made more readily and easily exceeds the limits of the anthropological realm alone.

The forensic anthropological context now goes hand in hand with the requirement for all methods to comply with scientific standards in a legal setting, such as the Daubert criteria (2,3). The methodological standards that must be met in order to fulfill this requirement were presented in several publications over the past ten years (2-6). They objectively highlight the need for standardized methodological sampling and statistical protocols for anthropological methods, in order for them to be valid and applicable in a forensic context. The authors of these papers provide guidelines to help with valid and standardized method construction: these guidelines concern both the samples and sampling protocols used to build the methods, such as the age, sex, and their distributions, and several statistical parameters, such as reliability, accuracy, or standard error of estimation. All of these parameters should 1/ be clearly presented in all publications and 2/follow these guidelines for methodological validity. Indeed, sampling and statistical parameters can be used to assess the methodological "quality" and the scientific and biological validity of the protocols of sub-adult age estimation methods. Because of the various approaches used to build these methods, the significant amount of publications and the methodological and statistical evolution that occurred since the first subadult age estimation method was published in the early 20 th century (1), it comes as no surprise that these methods can be extremely variable. Therefore, the accuracy and reliability of the resulting age estimates are quite variable and not always comparable.

The authors presented these standardized methodological guidelines as being crucial to the scientific validity of methods that would in turn ensure valid results. Therefore, these guidelines and the sampling and statistical parameters they deem as valid can be used to objectively evaluate methods and their protocols. A previous study has shown that, according to these parameters, a high number of methods do not comply with all recommendations [START_REF] Corron | A critical review of sub-adult age estimation in biological anthropology: do methods comply with published recommendations?[END_REF]. Standardization can also ensure that the estimates obtained with these methods are objectively evaluated and comparable to one another, using statistical parameters such as accuracy, reliability and standard estimation errors, when available. Methods that do not provide such parameters or do not comply with published recommendations should therefore be used with caution at least, or recalibrated or put aside at best. Protocol standardization and these methodological guidelines for building and applying methods are the main driver for the present anthropological tool: Sub-adult Aging Method Selection, or SAMS. SAMS is a decisional tool freely accessible through a webpage. It was developed to help anthropologists select the methods best adapted to their scientific goals referenced in a large database and directly and objectively evaluate the quality of these methods, according to the sampling and statistical recommendations presented above. SAMS can be found at the following address: osteomics.com/SAMS. Instructions on how to use SAMS can be found on the webpage, under the "About" tab.

MATERIAL AND METHODS

The database and parameters

A database referencing 261 publications available online through full-text archives of biomedical and life sciences journal literature and books used for sub-adult age estimation in bioarcheology and/or forensic anthropology was created. This living database can be updated with new publications or with missing information of already referenced methods. It was assembled and used for a study evaluating the validity of sampling and statistical protocols of sub-adult age estimation methods in forensic anthropology [START_REF] Corron | A critical review of sub-adult age estimation in biological anthropology: do methods comply with published recommendations?[END_REF][START_REF] Corron | Juvenile age estimation in physical anthropology: a critical review of existing methods and the application of two standardised methodological approaches[END_REF].

Publications were referenced by first author name and original year of publication (first and second author if they are only two, first author et al. if there are more than two). Each publication recorded in SAMS was characterized by several types of parameters: three relate to the anatomical element and age indicator used for age estimation (Table 1) and 19 parameters relate to their sampling, methodological and statistical parameters (Table 2). The first three parameters are selective: they are the ones used as search parameters to filter the database (see How SAMS works section, below). The 19 other parameters are illustrative and are used to characterize and/or evaluate the methods. The parameters were either explicitly presented in the original publications themselves, or they were presented in other reference works on sub-adults and age estimation that revised or tested the methods. If no information was provided in the original publication or other scientific articles using this publication, the parameter is attributed an "Unknown" descriptor (Table 2). To ensure uniformity and comparability between all methods and prevent bugging during processing, the parameters were standardized and simplified to be as binomial as possible. This ensures accuracy is maintained as much as possible while decreasing heterogeneity between categories without sacrificing the accuracy of the categories representing the parameters (see [START_REF] Corron | A critical review of sub-adult age estimation in biological anthropology: do methods comply with published recommendations?[END_REF] (1) for a full explanation on the process of building the database).

Table 1 Search parameters input by users to filter the database

Search parameters Definition

Anatomical Region

This parameter acts as a first filter for identifying the anatomical element(s) on which the age indicators will be obtained from. Several anatomical regions can be selected, depending on the number of elements users have at their disposal.

Bone

This parameter is essentially the selection of the skeletal and/or dental element from which the variables used for age estimation are obtained. It is either a single bone (e.g. humerus), several bones (e.g. limb bones: humerus, radius, ulna, femur, tibia and fibula), an anatomical zone (e.g. hand/wrist), or specific developmental states (e.g. deciduous teeth, mixed dentition, permanent teeth). First, the user selects the general anatomical region of interest which narrows the choices to the corresponding skeletal or dental elements of the selected region. Several elements can be selected for search.

Indicator Type

Indicator type refers to the predictor variables: they are either quantitative data, such as skeletal measurements, or qualitative data, such as skeletal or dental development or skeletal maturation stages. Depending on skeletal or dental preservation, the user can sometimes only use one type of indicator to predict age and methods are often based on one type or the other.

How SAMS works

The idea of SAMS was to build a "smart" classification of the methods in the database, that would a: comply with a user's choice of parameters; and b: comply with published methodological recommendations.

A SAMS user starts by selecting features for the three search parameters: "Anatomical Region", "Bone", "Indicator Type" to filter the database. Several features can be selected for each parameter (Figure 1). The user can also choose all, some, or none of the descriptive parameters to appear in the search results.

Table 2 Summary of the descriptive parameters of the publications and their respective features appearing in the results filtered by SAMS, as defined for the evaluation of the methods. Bolded features are considered valid according to methodological recommendations

Type of parameter

Parameter Features

Sampling

Sample size

Sample size was arbitrarily subdivided into five categories from less than a hundred individuals to more than a thousand:

[<100], [100-200], [200-500], [500-1000], [>1000].

Sample origin

The geographical origin of the individuals in the sample used to build the method, mainly relating to the country of origin of the individuals, but sometimes corresponding to an "ethnic" group or sub-population affiliation. The methods referenced in SAMS cover 29 countries so far.

Sample chronology

This parameter informs the user on the general chronological period the sample used to build the method dates from. The periods included are contemporary (the sample dates from the same time the method was published), modern (18 th and 19 th centuries), and archeological (17 th century and earlier).

Study Type

The type of study was either cross-sectional, semi-longitudinal or longitudinal. A cross-sectional study uses different and random individuals, who only contribute once to the sample, at a given point in time, by the developmental state they are in at that moment. One individual belongs to one age group. A longitudinal study follows the same individuals from a starting point to a finish point in time, and data is repeatedly collected at specific moments for each individual between these two dates.

Age Range

The precise age ranges of the individuals from the study sample on which the method was built. This parameter varies greatly from method to method, covering different ranges of ages from birth to adulthood. This gives an indication on whether the method is relevant to the user or not if the bone(s) available for estimation correspond to the methodological age range or not. The exact accuracy value of the age estimates of the corresponding method, when known.

Age

Standard error of estimation

-Known: standard error of estimation or the size of the prediction interval of estimated age is known -Unknown: standard error of estimation or the size of the prediction interval of estimated age is unknown

Detailed SEE

The exact value of the standard estimation error of age of the corresponding method, when known.

Repeatability

Validation of the method

-Validated method or data: the published data or age estimation method was validated by the authors using cross-validation and/or an independent test sample and/or was validated by an independent study -Non-validated method or data: no published validation of the method was found in the literature

Other

Sexed Method

Whether the method was built separately for males and females or not.

Two features: Yes or No

Variable Type

The specific variable type used for age prediction (e.g. bone measurement, dental mineralization stage, etc.) in the corresponding method. This parameter is extremely variable, and we advise the user to verify the methods' applicability in the original publication depending on the preservation of the elements available.

Medium of Study

The medium used for acquisition of the predictor variables in the corresponding method, such as dry bone, radiography, computed tomography, biochemical analysis, etc.

Result Type

This parameter is a general description of how the method is applied and how the results are obtained. It provides additional information on the method for the user, prior to its application. Result types were assigned into 6 categories:

-based on age per stage/score, -based on descriptive statistical parameters (mean, sd, frequencies, etc.), -based on regression equations, -based on atlas, abacus, tables, diagrams or growth curves, -based on probabilistic inference, -unknown.

The search parameters are the basis of the classification. The user selects features for the three search parameters relating to the anatomical element(s) at their disposal and the type of age indicator (quantitative, qualitative, both or either) they can obtain from the element(s) for age estimation. Descriptive parameters are different sampling, methodological and statistical characteristics presented in the publications and integrated in the database that provide additional information to help users with method selection and evaluation. The user can choose none or all of these features to appear in the results. After selecting the desired features, the user clicks on the "Filter" button to visualize the results. The database is then filtered following a simple similarity-based algorithm to provide the results. The algorithm behind SAMS SAMS uses a very simple similarity-based algorithm to compare user's input data to indexed publications and determine their relevance, which in this context means assess if the keywords regarding anatomical region and skeletal elements match those of the indexed methods. Relevance (R) is computed as follows: if the user-selected input matches the publications' keywords for anatomical region and bones involved, a value of 1 is attributed; if not, a value of 0 is given instead. The calculated average these comparison values gives the relevance value (R) in a normalized range between (not relevant) and 1 (highly relevant) for all 261 methods in the database. The same reasoning is applied to compute the validity value (V) of the methods by assessing the similarity of a given publication to each of the ten validity parameters (see Table 3). The final score (S) is given by the square root of the product of the relevance and validity values (S=√R*V). Only methods with a relevance higher than zero are retrieved.

RESULTS

The methods filtered using the features of the three search parameters and the algorithm behind SAMS appear as results on the page, along with any of the descriptive parameters the user has chosen to visualize (Figure 2). A URL provides a link to find the corresponding publications online.

The methods resulting from a query are classified from most to least compliant with the query via their "Relevance" value. The higher the "Relevance" of a method (the closer that value is to one), the closer the method is to the features the user selected for the three search parameters. A relevance of one means all the features of the three search parameters are the same as the features selected by the user. The methods are also classified from best to least compliant with published recommendations via their "Validity" value. The higher the "Validity" value (the closer that value is to one), the closer the methods comply with published sampling and statistical recommendations (see Table 3). A method with a validity value of one means that method complies with all valid features for the four sampling parameters and the five statistical parameters. The four valid sampling features are namely samples comprised of individuals of known age and known sex, with even age and sex distributions across age groups; the five valid statistical features are a reliability equal to or higher than 0.95, known accuracy, known standard estimation error, sufficiently low intra-and inter-observer errors, and the existence of some form of methodological validation.

Finally, each publication is awarded a "Score" value, which is the multiplication of the "Relevance" and "Validity" values (Figure 2 and Table 3). The higher the "Score" (the closer it is to one), the better the methods comply with both user demands ("Relevance") and methodological recommendations ("Validity").

DISCUSSION

Building SAMS -a compromise between reliability and precision of the parameters

The objectivity of the results is based on how the parameters of the methods were "translated" and categorized according to published recommendations. Standardization of these parameters to fit them into mainly binomial categories sometimes required simplification (see Corron et al. 2018, (1)). This is a common issue when dealing with categorical data in science [START_REF] Powers | Statistical methods for categorical data analysis[END_REF]. The authors made the choice to privilege reliability of the information by reducing the number of descriptors rather than precision, which would require a higher number of categories to describe the methodological parameters. Indeed, on top of decreasing the reliability of the results, the latter option of keeping a higher number of categories would also prevent direct comparison between the same methodological parameters of the methods in the output. This would go against the entire purpose of method classification and evaluation using common standardized parameters. This is why SAMS is presented as a guide to select, compare and evaluate methods. Once one or several methods are selected, the users are invited to visualize and read the selected publication(s) thoroughly in order to familiarize themselves with the details of the method(s) before applying it and including it in the report. URL links to access each method are presented in the output long with the descriptive parameters.

Table 3 Definitions of the result variables used to measure the relevance of the decisional tool and evaluate method compliance with published recommendations on method validity

Variables Definition

R: Relevance

Relevance corresponds to how much the methods are close to the features selected for the three search parameters selected: Anatomical region, Bone and Indicator type. The higher the Relevance (i.e. the closer it is to 1), the closer the method is to the user's selected search parameters.

V: Validity

Validity is how much the methods respect what are considered valid sampling and statistical parameters by the authors of methodological guidelines cited previously. In this context of juvenile age estimation, the valid parameters are the following:

-4 sampling parameters: samples with individuals of known age and known sex, even age and sex distributions across age groups. -5 statistical parameters: reliability = 0.95+, known accuracy, known SEE, sufficiently low intra-and inter-observer errors, and the existence of some form of methodological validation. Publications with high V values (i.e. closer to 1) comply the most with these methodological guidelines.

S: Score

Score = Relevance * Validity. This composite value gives an overall ranking of the publications according to R and V values. Publications with high S values are the ones that correspond to the users' choice AND respect methodological guidelines the most. Some methods, mainly older publications, presented incomplete or unclear sampling and/or statistical parameters were concerned. To account for this, the "Unknown" category was added to the selective parameters (see Corron et al. 2018, (1)). This can potentially modify the outcoming validity and score values if the unknown categories concern one or several of the nine parameters for which validity or invalidity was assessed using methodological recommendations [START_REF] Cunha | The problem of aging human remains and living individuals: a review[END_REF][START_REF] Ritz-Timme | Age estimation: the state of the art in relation to the specific demands of forensic practice[END_REF][START_REF] Rösing | Recommendations for the forensic diagnosis of sex and age from skeletons[END_REF][START_REF] Schmeling | Age estimation[END_REF]. If this information is incorrect, it can be easily fixed by the authors who can modify the dataset as soon as the correct information is made available to them and verified. Another limitation is the restricted access to publications. This is either because publications have not been digitized or are not available online, especially for older methods, or because of the language barrier, preventing the authors from including works originally published in languages other than English or French. If additional publications are made available online or directly sent to the authors, they could easily be integrated to the database and increase the number of references. SAMS can therefore stay relevant and improve with time through the collaborative effort of anthropologists who aim to standardize methods and share knowledge around the world.

With the wide array of methods or approaches available to anthropologists for estimating subadult age comes some level of variation in terms of sampling and statistical parameters and statistical approaches. If some of these parameters, such as the nine ones we selected as classifying parameter for SAMS, can effectively be categorized as valid or invalid based on published recommendations, it is more difficult to so confidently assign a valid or invalid stamp for the type of statistical approach used to estimate age. In our sample of 261 publications, the "Result type" parameter was reduced to five categories (Table 2) which themselves each cover between one and five different statistical approaches.

Although some publications have looked into determining which statistical approach is the "best" for age estimation using ordinal or continuous data [START_REF] Corron | A critical review of sub-adult age estimation in biological anthropology: do methods comply with published recommendations?[END_REF][START_REF] Konigsberg | Estimation and evidence in forensic anthropology: age-at-death[END_REF][START_REF] Konigsberg | Optimal trait scoring for age estimation[END_REF][START_REF] Nayyar | Age estimation: current state and research challenges[END_REF], they do not provide a consensual or clear answer that is applicable for all indicators of age, and/or all ages and/or all contexts (e.g. forensic or bioarcheological). This does not seem surprising based on the variety of data these methods are based on. The different levels of variation behind the age indicators themselves also probably play a role in these diversified approaches. Because of these limitations, we chose not to classify "Results type" as a classification parameter for method evaluation. However, because of its unequivocal value for method selection, we decided to keep it as a descriptive parameter in SAMS that the user could choose to use as a selective parameter if desired. As more papers are published on subadult age estimation, this parameter, and others, could ultimately be included as classifying parameters to improve the accuracy of the relevance and validity values to better qualify the methods.

Using SAMS -Applications and limitations

The main goal of SAMS is to provide anthropologists with a practical analytical tool that follows recommendations for building and applying subadult age estimation methods in the forensic context in order to assist practitioners. This type of approach could of course be adapted to any type of method, be it adult age estimation, sex estimation, or other, and could be based on other recommendations or protocols. Presenting SAMS as a free tool available online facilitates the dissemination of all the methods included in this database to all bioanthropological and medical contexts. To our knowledge, there is no tool comparable to SAMS available in the forensic anthropology or larger bioanthropological community. Several GUIs have been developed these past few years to facilitate practical method application and dissemination, but they are all method-specific and are not a meta-analytical decisional tool.

By helping decipher and evaluate published methods based on common standardized parameters, SAMS also helps clearly present the advantages and limits of each method appearing as a query result. These advantages and limits are elements that should be included in technical reports, especially in the forensic context. By dissecting each element of a method's sampling and statistical protocols, SAMS provides direct criticism of that method to help understand and evaluate the methodology behind the publication for more objective and critical report writing. A better understanding of the method by the practitioner will improve the clarity of the report and a better understanding of the methods by the judicial figure reading the report. By allowing for protocol transparency and standardization, SAMS enables both the practitioner and the judge to easily evaluate whether a method complies with the Daubert criteria or not. Indeed, SAMS acts as a screening process allowing each methodological parameter to be deciphered and separated, so the defaulting or limiting parameter(s) for each method can be easily spotted. The relevance and validity values emphasize this evaluation by objectively providing statistical arguments to the confidence one can have in a method's quality, i.e. how much it complies with the Daubert recommendations.

Although SAMS is based on the methodological rigor necessary in the forensic context, it is also useful in the archeological context. Its main appeal lies in the wide range of methods included in it that can be selected. Indeed, the main issue in bioarcheology is the variation in skeletal preservation [START_REF] Gordon | Soil pH, Bone Preservation, Sampling Bias at Mortuary Sites[END_REF]. As a database compiling methods for almost every anatomical element of the skeleton, SAMS increases the chances of finding one or several methods compatible with the available remains. Moreover, it provides a better visibility for methods that are sometimes forgotten or not well-disseminated (e.g. methods published in regional/country-specific journals, older methods unavailable online). Overall, SAMS helps broaden the methodological possibilities of subadult age estimation, which is beneficial for the study of subadult individuals in bioarcheological, forensic or pediatric contexts.

CONCLUSIONS

SAMS provides a quick and easy way to help forensic anthropology practitioners and bioarcheologists select and evaluate an ensemble of sub-adult age estimation methods. It provides relevant information related to sampling and statistical parameters of these methods to verify whether they comply with published recommendations on anthropological methods in a forensic setting through their "Validity" scores.

SAMS will continue to be implemented with new publications to ensure it stays relevant and up to date as more and more sub-adult age estimation methods are published. Bi-annual updates are planned to input new relevant publications.

As we aim to adopt a collaborative approach, we welcome any scientific support the anthropology community can make to complete, correct and update this work.

Fig. 1 .

 1 Fig. 1. Example of a SAMS query. This query uses several features of the three search parameters to filter the database and part of the descriptive parameters that will appear in the results.

Fig. 2 .

 2 Fig. 2. Results of the query obtained by filtering the database using the SAMS algorithm. The features of the three search parameters are presented at the top. Users can evaluate the methods using the Relevance (R), Validity (V) and Score.

-

  Yes: the distribution of the number of individuals per age group (monthly, annual) in the study sample is even -No: the distribution of the number of individuals per age group (monthly, annual) in the study sample is not even -Unknown: the distribution of the number of individuals per age group (monthly, annual) in the study sample is unknown

	Uniformity of
	age
	distribution
	Uniformity of
	sex
	distribution
		-Known: chronological age is known for all individuals in the study
		sample
		-Unknown: chronological age is not known for any individual in
		the study sample. It is often estimated beforehand using a
		different method
		-Known and unknown: chronological age is known for some
		individuals in the study sample
		-Known: sex is known for all individuals in the study sample
	Sex	-Unknown: sex is not known for any individual in the study sample -Known and unknown: sex is known for some individuals in the
		study sample

-

  Yes: the distribution of the number of individuals per age group (monthly, annual) in the study sample is even for both sexes -No: the distribution of the number of individuals per age group (monthly, annual) in the study sample for both sexes is not even -Unknown: the distribution of the number of individuals per age group (monthly, annual) in the study sample for both sexes is unknown

	Reliability of estimated age	-≥ 95%: reliability of estimated age is equal to or higher than 95% -< 95%: reliability of estimated age is lower than 95% -Unknown: reliability of estimated age is unknown
	Accuracy of	-Known: accuracy of estimated age is known
	estimated age	-Unknown: accuracy of estimated age is unknown
	Detailed
	accuracy
	Statistical

and reproducibility -Sufficient repeatability and reproducibility: repeatability

  and reproducibility of the age indicators used for age estimation were both tested beforehand or in a different publication -Sufficient repeatability: repeatability of the age indicators used for age estimation was tested beforehand -Sufficient reproducibility: reproducibility of the age indicators used for age estimation was tested beforehand or in a different publication -Untested repeatability and reproducibility: neither repeatability or reproducibility of the age indicators used for age estimation were tested -Unknown: repeatability and reproducibility is unknown

Acknowledgments

The authors wish to thank Prof Eugenia Cunha and Dr Kyra Stull for their help and advice on how to build SAMS and Ms Laura Cirillo, Dr Aurore Lambert, Dr Sandy Parmentier, Dr Emeline Verna and Ms Tatiana Vlemincq-Mendieta for their feedback.

CRediT authorship contribution statement