Jonathan Prunier

Laurence Maurice

Emile Perez

Julien Gigault

Anne-Catherine Pierson Wickmann

Mélanie Davranche

Alexandra Ter Halle

Trace metals in polyethylene debris from the North Atlantic subtropical gyre

Keywords: Microplastic, plastic debris, metals'accumulation, polyethylene, polymer

Plastic pollution in the marine environment poses threats to wildlife and habitats through varied mechanisms, among which are the transport and transfer to the food web of hazardous substances. Still, very little is known about the metal content of plastic debris and about sorption/desorption processes, especially with respect to weathering. In this study, plastic debris collected from the North Atlantic subtropical gyre was analyzed for trace metals; as a comparison, new packaging materials were also analyzed.

Introduction

Reports of plastic debris found in marine and terrestrial habitats have led to a growing awareness of the hazards from plastic pollution [START_REF] Rochman | Classify plastic waste as hazardous[END_REF] . Subtropical gyre accumulations are emblematic [START_REF] Law | Distribution of Surface Plastic Debris in the Eastern Pacific Ocean from an 11-Year Data Set[END_REF] , but plastic pollution is found throughout the oceans, as well as in bays [START_REF] Schlining | Debris in the deep: Using a 22-year video annotation database to survey marine litter in Monterey Canyon, central California, USA[END_REF] , estuaries [START_REF] Bakir | Transport of persistent organic pollutants by microplastics in estuarine conditions[END_REF] and terrestrial environments such as rivers [START_REF] Eerkes-Medrano | Microplastics in freshwater systems: A review of the emerging threats, identification of knowledge gaps and prioritisation of research needs[END_REF] and lakes [START_REF] Eriksen | Microplastic pollution in the surface waters of the Laurentian Great Lakes[END_REF] . However, the research remains young, and we need to better understand the effect of plastic pollution on ecosystems. The direct impacts of plastic on wildlife are entanglement and ingestion [START_REF] Wright | The physical impacts of microplastics on marine organisms: A review[END_REF] .

Instances of ingestion that result in ulceration or starvation are increasing and are found throughout the ecosystem [START_REF] Gall | The impact of debris on marine life[END_REF] . As an indirect impact, plastic debris is transported by ocean

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3 currents over very large distances and serves as a novel habitat for various organisms, leading to the spreading of biological species across oceans [START_REF] Goldstein | Relationship of diversity and habitat area in North Pacific plastic-associated rafting communities[END_REF][START_REF] Zettler | Life in the "Plastisphere": Microbial Communities on Plastic Marine Debris[END_REF][START_REF] Carlton | Tsunami-driven rafting: Transoceanic species dispersal and implications for marine biogeography[END_REF] . Another concern is the chemical pollution associated with plastic debris [START_REF] Engler | The Complex Interaction between Marine Debris and Toxic Chemicals in the Ocean[END_REF] . Various classes of chemicals are carried by plastics; some are listed as priority pollutants, and others are even classified as toxic substances [START_REF] Rochman | Classify plastic waste as hazardous[END_REF][START_REF] Rochman | Long-Term Field Measurement of Sorption of Organic Contaminants to Five Types of Plastic Pellets: Implications for Plastic Marine Debris[END_REF][START_REF] Lithner | Environmental and health hazard ranking and assessment of plastic polymers based on chemical composition[END_REF] . However, identifying all the substances that could be transported by plastics is far from easy, and the transfer of these substances to the marine environment and biota has only been approached to a limited extent [START_REF] Rochman | Early warning signs of endocrine disruption in adult fish from the ingestion of polyethylene with and without sorbed chemical pollutants from the marine environment[END_REF][START_REF] Tanaka | Accumulation of plastic-derived chemicals in tissues of seabirds ingesting marine plastics[END_REF][START_REF] Wardrop | Chemical Pollutants Sorbed to Ingested Microbeads from Personal Care Products Accumulate in Fish[END_REF] .

To better understand the interaction of plastic debris with chemicals and toxic elements, it is important to first differentiate plastics from polymers. The term polymer refers to the macromolecule, whereas the term plastic describes a manufactured product. A plastic is a material made of a polymer (or sometimes a mixture of several polymers) to which additives have been added to produce the required properties for a given application [START_REF]Emission scenario document on plastic additives[END_REF] . The chemicals transported by plastics can be categorized according to their origin: 1) molecules from the polymerization process, 2) compounds introduced during the formulation step, namely, the additives, and 3) molecules or elements sorbed onto plastic during the use of the item or during its stay in the environment. All these chemicals are not covalently bound to the polymer, and sorption and desorption processes are involved; these processes are complex mechanisms influenced by external conditions such as pH, the salinity of the surrounding media [START_REF] Turner | Concentrations and Migratabilities of Hazardous Elements in Second-Hand Children's Plastic toys[END_REF][START_REF] Turner | Mobilisation kinetics of hazardous elements in marine plastics subject to an avian physiologically-based extraction test[END_REF] , UV light and shear stress, but the two latter factors are seldom considered.

Regarding the metal content in plastic debris, the most attention has been paid to beached preproduction pellets [START_REF] Rochman | Long-Term Field Measurement of Sorption of Organic Contaminants to Five Types of Plastic Pellets: Implications for Plastic Marine Debris[END_REF][START_REF] Holmes | Interactions between trace metals and plastic production pellets under estuarine conditions[END_REF][START_REF] Rochman | Long-Term Sorption of Metals Is Similar among Plastic Types: Implications for Plastic Debris in Aquatic Environments[END_REF][START_REF] Holmes | Adsorption of trace metals to plastic resin pellets in the marine environment[END_REF][START_REF] Ashton | Association of metals with plastic production pellets in the marine environment[END_REF][START_REF] Brennecke | Microplastics as vector for heavy metal contamination from the marine environment[END_REF] . Preproduction pellets (from 1 mm to 5 mm) are generally made of raw resin and used in the manufacturing of plastics, where they are melted into objects. Although pure polymers are generally acknowledged to be rather inert toward ions, the sorption of trace metals by new and beached preproduction PE pellets has been 21 23, 24 . It has been demonstrated that metal sorption is enhanced with pellet weathering 13 24 ; the increases in polarity, surface area and porosity promote trace-metal binding [START_REF] Ashton | Association of metals with plastic production pellets in the marine environment[END_REF] . The accumulation of metals may also be mediated by biofilm development [START_REF] Rochman | Long-Term Field Measurement of Sorption of Organic Contaminants to Five Types of Plastic Pellets: Implications for Plastic Marine Debris[END_REF] .

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Imhof et al. recently reported the presence of paint particles in freshwaters [START_REF] Imhof | Pigments and plastic in limnetic ecosystems: A qualitative and quantitative study on microparticles of different size classes[END_REF] . They demonstrated that the proportion of paint among microplastics in Lake Granada (Italy) was increased in smaller plastic debris (in the range 50 -500 µm) and that the debris contained a high variety of metals such as Cd, Pb and Cu. Most of field data regarding metal sorption onto plastic concerns beached pellets, but Turner et al. developed a field survey using portable XRF and analyzed beached plastic debris [START_REF] Turner | Analysis of the elemental composition of marine litter by field-portable-XRF[END_REF][START_REF] Turner | Trace elements in fragments of fishing net and other filamentous plastic litter from two beaches in SW England[END_REF][START_REF] Turner | Heavy metals, metalloids and other hazardous elements in marine plastic litter[END_REF] . In comparison, the concentrations of several metals were two to three orders of magnitude higher in plastic debris than in pellets (e.g., Cd concentrations were between 1.09 and 76.7 ng/g in beached pellets [START_REF] Holmes | Adsorption of trace metals to plastic resin pellets in the marine environment[END_REF][START_REF] Ashton | Association of metals with plastic production pellets in the marine environment[END_REF] , while values ranged from 25 to 147 µg/g in beached debris [START_REF] Turner | Analysis of the elemental composition of marine litter by field-portable-XRF[END_REF] ; all samples were from England).

Regarding the transfer of metals to the trophic chain, it has been suggested that metals are mostly sorbed onto the pellet surface and are therefore likely bioavailable by ingestion [START_REF] Ashton | Association of metals with plastic production pellets in the marine environment[END_REF] .

High levels of ingested plastic in sea birds have been recently correlated with increasing concentrations of Cl, Fe, Pb, Mn and Rb in feathers [START_REF] Lavers | Ingested plastic as a route for trace metals in Laysan Albatross (Phoebastria immutabilis) and Bonin Petrel (Pterodroma hypoleuca) from Midway Atoll[END_REF] . These preliminary studies suggest that the transfer and accumulation of trace metals by plastic debris must be further investigated.

In the present study, we analyzed the trace metal content in plastic debris collected from the North Atlantic subtropical gyre. This is the first report of trace metal content in plastic debris from the open ocean. A large variety of plastics are present in the environment [START_REF] Anderson | Sources, fate and effects of microplastics in the marine environment: a global assessment[END_REF] , but the most abundant in the open ocean is polyethylene (PE) [START_REF] Moret-Ferguson | The size, mass, and composition of plastic debris in the western North Atlantic Ocean[END_REF][START_REF] Ter Halle | Understanding the Fragmentation Pattern of Marine Plastic Debris[END_REF] ; the discussion is thus focused on PE. For comparison, we also analyzed preproduction pellets and new packaging materials to understand whether the metals found in debris result from

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manufacturing, formulation or sorption that occurs after release to the environment. Trace metal contents are discussed in terms of the oxidation state, size and color of the debris.

The concentrations measured here are compared to published data for beached debris and for debris from coastal areas [START_REF] Rochman | Long-Term Field Measurement of Sorption of Organic Contaminants to Five Types of Plastic Pellets: Implications for Plastic Marine Debris[END_REF][START_REF] Holmes | Interactions between trace metals and plastic production pellets under estuarine conditions[END_REF][START_REF] Holmes | Adsorption of trace metals to plastic resin pellets in the marine environment[END_REF][START_REF] Ashton | Association of metals with plastic production pellets in the marine environment[END_REF][START_REF] Brennecke | Microplastics as vector for heavy metal contamination from the marine environment[END_REF][START_REF] Turner | Analysis of the elemental composition of marine litter by field-portable-XRF[END_REF] and rivers [START_REF] Wang | Microplastics in the surface sediments from the Beijiang River littoral zone: Composition, abundance, surface textures and interaction with heavy metals[END_REF] .

EXPERIMENTAL SECTION

Sample origin

Mesoplastics (0.5 -20 cm) were collected from a boat in the North Atlantic subtropical gyre in May 2014 and June 2015 during the French 7 th Continent Expedition. The mesoplastics were visible from the sailing vessel Guyavoile and floated at the sea surface.

Thirteen PE mesoplastics were selected for trace metal analysis, as they covered a large range of sizes, colors and types of items; a complete description is given in Table SI1. The mesoplastics were cut into small pieces with a ceramic knife for purpose analysis, stored in sealed plastic bags and frozen at -5°C until analysis. Microplastics (0.1 -0.5 cm) were collected in a manta net equipped with a mesh size of 300 µm. On the boat, 5 mixtures of microplastics were prepared; for each mixture, a total of 15 microplastics were collected with a plastic tweezer and placed in a plastic bag that was sealed and stored at -5°C. The GPS locations of the four net tows are given in Table SI2. Each mixture of microplastics was chosen to be representative of the mixture usually collected in the survey area in terms of size and color. The average length and weight of the microplastics collected in this area were 1.5 mm (±0.3 mm) and 2 mg (±0.2 mg), respectively. The samples were mostly white (80%). Several fibers were also present in the samples; the fibers were approximately one millimeter in diameter and were attributed to fishing lines because clothing fibers are typically thinner [START_REF] Ter Halle | Understanding the Fragmentation Pattern of Marine Plastic Debris[END_REF] . For Polyethylene virgin pellets, six samples of PE (CAS 9002-88-4) (also referred to as prefabrication pellets) were obtained as reference materials. Three PE

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ACCEPTED MANUSCRIPT 6 pellets were purchased from Sigma Aldrich (Saint Louis, MO, USA), and three were purchased from Goodfellow (Huntingdon, UK). A full physicochemical characterization of these pellets is given elsewhere [START_REF] Ter Halle | To what extent are microplastics from the open ocean weathered?[END_REF] . Twenty-two new PE packaging materials (high density and low density) were randomly selected from supermarkets; items such as milk bottles, white and colored plastic containers, tubes and caps were analyzed for their trace metal contents. These items are described in the Supporting Information (Table SI3). A full physicochemical characterization of these pellets is given in ter Halle et al. [START_REF] Ter Halle | To what extent are microplastics from the open ocean weathered?[END_REF] .

Fourier transform infrared (FTIR) spectroscopy

Infrared spectra were recorded using a Thermo Nicolet Nexus spectrometer equipped with a diamond crystal ATR accessory and a deuterated triglycine sulfate (DTGS) detector. A full description of the procedure is given elsewhere, together with the method used to calculate the carbonyl index (I CO ) [START_REF] Ter Halle | To what extent are microplastics from the open ocean weathered?[END_REF] .

Trace metal analysis

Each plastic sample (between 35 and 130 mg) was solubilized (in duplicate for several samples) by acid digestion with bi-distilled HNO 3 using a multistep procedure with a microwave oven (MARS 5 system CEM; 110°C -3 min, then 160°C -3 min and 200°C -15 min). Trace element concentrations were obtained by measurement on a quadrupole ICP-MS (inductively coupled plasma mass spectrometer, Agilent 7500, ®Perkin Elmer) at the GET laboratory (Toulouse, France). The international geostandard ERM-EC680K PE (low level) was used to check the validity and reproducibility of both the acid digestion and ICP-MS analyses. Indium and rhenium were used as internal standards for the ICP-MS measurements. Blank tests indicated that the level of contamination induced by the acid digestion procedure was negligible. The total Hg concentration (Hg T ) was measured in duplicate or triplicate by atomic absorption spectrometry (Advanced Mercury Analyzer,

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, ®LECO) after combustion and gold-trapping directly on the dried plastic debris.

The recoveries of Zn, Cd, Sb, Pb and Hg from the ERM-EC680K standard were close to 100%, whereas those of Cr, As and Sn were 55%, 63%, and 53%, respectively, suggesting that the measured Cr, Sn and As concentrations may be slightly underestimated in the microplastic debris. Limits of detection were respectively 91 and 11 ng.g -1 for Ca and K, 0.85 ng.g -1 for Al and Zn, 0.64 ng.g -1 for Fe, respectively 0.27 and 0.12 ng.g -1 for Cr and Sn, between 0.01 to 0.08 ng.g -1 for Cd, Co, Ti, As, Sr, Ba, Sb, Mn, V, Ni, Cu and Pb, and lower than 0.01 ng.g-1 for Mo, Th and U.

Transmission electron microscopy

Transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) studies were performed using a JEOL cold-FEG JEM-ARM200F operated at 200 kV and equipped with a probe Cs corrector reaching a spatial resolution of 0.078 nm.

Energy-dispersive X-ray (EDX) spectra were recorded on a JEOL CENTURIO SDD detector. To visualize the presence of metals on the MD0518-4 sample, the surface of the debris was gently scrubbed. The extracted particles were analyzed by TEM/EDX. More than 150 observations were collected.

Soda washing of the mesoplastic

In a Teflon beaker at room temperature, 100 mg of the mesoplastic 14MD0518-4 was mixed with 5 mL of a NaOH solution (1 mol L -1 ) for 48 hours. A blank control was subjected to the same experiment without mesoplastic. The sample mass balance after washing exhibited a loss of 5.14 mg. The treated mesoplastic was analyzed for trace metal content according to the same protocol. The mesoplastics and NaOH solution were evaporated at 70°C, and the residues were collected in a HNO 3 solution (10 M) for ICP-MS analysis.

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Statistical analyses

Multivariate statistical analysis (MSA), including principal component analysis (PCA), hierarchical cluster analysis (HCA) Pearson or Spearman's correlation analysis, is largely used in environmental studies to explore the origins of major or trace elements by reducing the dataset to the main influencing factors (called "principal components"), based on the similarities and differences among samples and relationships among variables in order to highlight the natural and/or anthropogenic origins of elements in different reservoirs (river waters, soils, rocks, mineral water bottles, etc.). Principal component analysis uses an orthogonal transformation to convert possibly correlated variables into a set of independent variables called ''principal components'' (PCs), allowing the accurate description of complex systems. The rows of the starting data matrix for PCA analysis represent the samples (i.e., 42

RESULTS AND DISCUSSION

Metal concentrations in PE virgin pellets

The trace metal concentrations in PE virgin pellets were almost all below the µg.g -1 level or the ICP-MS detection limit, except for Ti, Cr and Mo (Table SI4). Titanium concentrations reached 5 µg.g -1 in HDPE-3 mm but were an order of magnitude lower in the other samples. The chromium concentration reached 10 µg.g -1 in LDPE-1 mm pellets; this sample also presented 23 µg.g -1 of Mo. This value is approximately 10 times greater than those observed for the other samples.

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Considering the molecules originating from the polymerization processes, in addition to free radical initiation, polyethylene (PE) is polymerized using transition-metal catalysts.

Catalytic polymerization accounted for approximately 73% of the global PE production in 2008 [START_REF] Murphy | Additives for plastic handbook[END_REF] . Transition-metal catalysts used for PE production include i) Ziegler-Natta, ii) supported Cr and iii) single-site catalysts. Ziegler-Natta catalysts are derived from inorganic Ti compounds (activated with organo-Al compounds). The most common supported Cr catalysts are the so-called "Phillips catalysts", which are supported on refractory oxides, most often silica, and contain small amounts of Mo [START_REF] Murphy | Additives for plastic handbook[END_REF] . Most commercial single-site catalysts include Zr, Hf or Ti; the use of transition metals such as Pd, Fe or Ni emerged in the mid-1990s.

These noticeable amounts of Cr and Mo indicate that the sample LDPE-1 mm was probably synthesized using a Cr-supported catalyst. For the other samples, we could not clearly determine which catalyst had been used, as no trace metals appeared at a high concentration.

Metal concentrations in new packaging materials

In total, 23 packaging materials (for cosmetics, food, beverages, etc.) randomly selected from everyday life were analyzed; these items were made of high-or low-density PE.

These PE packaging materials showed very scattered metal concentrations (Table SI5). On average, there were no significant differences between HDPE and LDPE packaging. All metal concentrations were higher in the packaging materials than in the virgin pellets.

There is one exception, a LDPE-1 mm pellet that presented high Cr and Mo contents.

Antioxidants and UV stabilizers are often based on Ti (usually added at values between 0.5 and 5% w/w 37 ). Other metals, such as Cd, Zn, Ba and Pb, are used as stabilizers in PE packaging in typical amounts between 0.05 and 3% w/w [START_REF]Hazardous substances in plastic materials[END_REF] . Pigments are usually

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10 incorporated in plastics in proportions between 0.01 and 10% w/w; mixed metal oxides based on Ti, Cr, Ni, Sb, Mn, Co, Al, Zn, Fe or Cu are often used as pigments [START_REF] Murphy | Additives for plastic handbook[END_REF] . For example, Cu is used in the form of copper oxides, St is used in the nitrate or chlorate form as a red colorant [START_REF] Bylan | Developments in colorants for plastics[END_REF] , and chromates and cadmium sulfoselenide are used as orange colorants [START_REF] Marier | Polypropylene: the Definitive User's Guide and Databook[END_REF] . Fillers are typically made of calcium carbonate or zinc oxide and can be incorporated at up to 50% w/w [START_REF] Lazzeria | Filler toughening of plastics. Part 1-The effect of surface interactions on physico-mechanical properties and rheological behaviour of ultrafine CaCO3/HDPE nanocomposites[END_REF] . For example, Ba sulfate is a white pigment that promotes brightness. However, this additive is also an inert white filler that promotes resistance to acid and alkalis and provides very good weathering resistance [START_REF] Murphy | Additives for plastic handbook[END_REF] . Zinc distearate is used as a stabilizer in the polymer industry and as a lubricant in the manufacturing of plastic moldings in industrial processes. Some metal-based compounds are used as antimicrobial agents; Cu, Mn, Co, Ni, Zn, Ag and Cd are used in sulfonamides, and organotin compounds are used as biocides in plastic [START_REF] Rizzotto | Chapter 5 Metal complexes as antimicrobial agents In A search for antibacterial agents[END_REF] . In Europe, Pb has been phased out as a plastic additive [START_REF] Murphy | Additives for plastic handbook[END_REF] , and Cd use is still under discussion; according to the Restriction of Hazardous Substances (RoHS) Directive for any new or recycled product, the concentration limits for Cd are set at 100 µg.g -1 [START_REF]Restriction of hazardous Substances[END_REF] . Additionally, potentially hazardous elements are restricted (As, Ba, Se and Sn) because of their properties of migration from plastics [START_REF] Cordeiro | IMEP-34: Heavy metals in toys according to EN 71-3[END_REF] . A Norwegian report has mapped the prioritized hazardous substances used in plastic materials [START_REF] Hansen | Hazardous substances in plastic materials, COWI and the Danish technological institute on behalf of thr Norwegian Climate and Pollution Agency[END_REF] : arsenic (antimicrobial and plasticizer), Br (brominated flame retardant), Cd (pigment; heat-and UV-stabilized only in PVC), Cr (catalyst and pigment) Co (light-blue pigment in PET), B (buffering agent), Pb (pigment; heat-and UV-stabilized in PVC), Hg (catalyst for polyurethane), organic tin compounds (biocides), etc. These inorganic additives confer a wide range of interesting properties to plastic for various applications. It seems challenging to replace metal-based pigments with organic ones, especially from an economic point of view; furthermore, organic pigments do not present the same stability as inorganic ones [START_REF] Murphy | Additives for plastic handbook[END_REF] .

In the literature and in legislative documents, it is stated that the migration rate of

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inorganic metal salts is very limited compared to those of other substances (like monomers, residual solvents or other even larger organic molecules) [START_REF] Murphy | Additives for plastic handbook[END_REF][START_REF]Hazardous substances in plastic materials[END_REF] . However, migration evaluation tests are performed on new plastics; there are no data under advanced weathering conditions such as those encountered by marine plastic debris. Titanium concentrations in the packaging materials ranged between 0.4 and 849.5 µg.g -1 , with a median value of 140.9 µg.g -1. This corresponds to typical loads of Ti in PE plastics [START_REF] Murphy | Additives for plastic handbook[END_REF] . Copper contents were significantly higher in the colored (150 µg.g -1 on average) than in the white packaging materials (0.44 µg.g -1 on average) or the pellets (0.09 µg.g -1 ).

The average Ba concentration was particularly high in the colored packaging materials (225 µg.g -1 ), while the Ba values were below 1 µg.g -1 in the white items and in the virgin pellets. The highest Ba concentrations were obtained for 5 packaging materials, which corresponded to rigid lids or caps with dark colors (red, purple, brown). Lead concentrations in the packaging materials were above 1 µg.g -1 (2.25 and 1.6 µg.g -1 ) for only 2 items, and Cd concentrations were below 0.83 µg.g -1 . Zinc concentrations ranged from 0.06 to 0.25 µg.g -1 in the PE virgin pellets but reached up to 331 µg.g -1 in a brown LDPE lid and averaged 25.9 µg.g -1 in the packaging materials. Strontium concentrations in the packaging materials ranged from LD (0.05 µg.g -1 ) to 56 µg.g -1 in a red LDPE soda sample, while Sr values for the prefabrication pellets were below the detection limit.

Metal concentrations in plastic debris from the North Atlantic subtropical gyre

Plastic debris were characterized by FTIR spectroscopy and were mostly made of polyethylene (90%) and in the present study the discussion is focused on polyethylene. The total metal concentrations varied strongly from one debris material to another (Table SI6 andSI7). The complexity of the metal mixtures found on plastic debris has already been addressed by Rochman et al. [START_REF] Rochman | Long-Term Field Measurement of Sorption of Organic Contaminants to Five Types of Plastic Pellets: Implications for Plastic Marine Debris[END_REF] . Globally, the metal concentrations were higher in the plastic debris

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(mesoplastics and microplastics) than in the new packaging materials, except for Ba, Co, Cu, and Sn.

The average Ti concentration in the plastic debris (1404 µg.g -1 ) was significantly higher than that in the packaging materials (222.5 µg.g -1 , Figure 1). Several hypotheses could explain these results: 1) the plastic debris derived from old manufactured objects, and their dated formulation used higher amounts of Ti; 2) titanium is a UV stabilizer, and the debris materials that persist the most might be those containing the highest amount of Ti; and 3) the Ti present in the debris resulted from sorption.

Vanadium, Ni and Sr concentrations in the plastic debris were significantly higher than in the new packaging materials. As these trace metals have not been identified as plastic additives, their presence might therefore result from sorption processes. Arsenic contents were significantly higher in the plastic debris (from 0.1 to 0.8 µg.g -1 ) than in the new packaging materials (0.05 µg.g -1 ). Arsenic was once commonly used in formulations, but its use is now restricted [START_REF] Hansen | Hazardous substances in plastic materials, COWI and the Danish technological institute on behalf of thr Norwegian Climate and Pollution Agency[END_REF] . High concentrations of As in the plastic debris might thus result from the age of the manufactured objects from which the mesoplastics were derived or from a sorption process.

Zinc contents were higher in the plastic debris (average 54 µg.g -1 ) than in the new packaging materials (26 µg.g -1 ). The presence of zinc might be attributed to additives (pigments, fillers, etc.) and/or sorption processes. The molybdenum concentration was 10 times higher in plastic debris than in new packaging materials. Molybdenum is largely used in catalyst formulations; however, this increase suggests sorption processes. The cadmium concentration was the highest in the mesoplastic 14MD0518-4, reaching 4284 µg.g -1 in this material and an average of 278 µg.g -1 in the plastic debris, but less than 0.012 µg.g -1 in the packaging materials. The cadmium content in the plastic debris thus seems to be accounted for by sorption. Regarding

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13 the exception of 14MD0518-4, this object might have been a container for a product enriched in Cd, such as a phytopharmaceutical product [START_REF] Nziguheba | Inputs of trace elements in agricultural soils via phosphate fertilizers in European countries[END_REF][START_REF] Jiao | Environmental risks of trace elements associated with long-term phosphate fertilizers applications: A review[END_REF] . The average Ba concentration in the plastic debris was 70.9 µg.g -1. In the packaging materials, the Ba concentration was very high in two samples: the purple P30 (1538 µg.g -1 ) and brown P7 (871 µg.g -1 ) samples; indeed, Ba is often used in red and brown pigments. Excluding those two extreme values, the concentration of Ba was 5 times higher in the mesoplastics than in the packaging materials. The same observation can be made for Sn, with a high value in the green P24 packaging material that distorted the calculation of the average concentration. Without those extreme samples, the Sn concentration was 2 times higher in the plastic debris than in the packaging materials. Both elements highlight not only the heterogeneity of the samples but also the complexity of the mechanisms involved in the trace metal presence in plastic debris. Titanium, V, Ni, Zn, As, Sr, Mo, Cd and Ba concentrations were higher in the plastic debris than in the packaging materials; the ratio of average concentrations ranged between 2 (for Sn) and 190 000 (for Cd, see Figure SI1). The present data were compared to the literature data for coastal areas (Table SI8). Generally, the mesoplastics collected in coastal areas [START_REF] Turner | Analysis of the elemental composition of marine litter by field-portable-XRF[END_REF][START_REF] Massos | Cadmium, lead and bromine in beached microplastics[END_REF] and here in the open ocean presented higher trace metal contents (specifically Cd and Cr) than did beached pellets [START_REF] Holmes | Adsorption of trace metals to plastic resin pellets in the marine environment[END_REF][START_REF] Ashton | Association of metals with plastic production pellets in the marine environment[END_REF] . For example, the Zn content in plastic debris was more than 36-fold higher than in beached pellets. These differences can be explained by the higher initial metal amount in plastics than in raw pellets but also by the higher sorption capacity of plastics, due to their higher polarity conferred by additives, than that of prefabrication pellets. The general tendency is that the mesoplastics from English coastal areas [START_REF] Turner | Analysis of the elemental composition of marine litter by field-portable-XRF[END_REF][START_REF] Massos | Cadmium, lead and bromine in beached microplastics[END_REF] or Chinese littoral areas [START_REF] Wang | Microplastics in the surface sediments from the Beijiang River littoral zone: Composition, abundance, surface textures and interaction with heavy metals[END_REF] and the ones from the open ocean here present the same order of magnitude of contamination. This result is remarkable and different compared with data for organic pollutants, whose concentration is lower in plastic debris from the open ocean than in that from coastal areas [START_REF] Hirai | Organic micropollutants in marine plastics debris from the open ocean and remote and urban beaches[END_REF] . shown). Most metal concentrations significantly decreased after washing (Figure 2 and Table SI9). All Ti and Cd were removed (99 to 100%), and almost all Zn (97%) and 80% of V were leached (V concentrations dropped from 4.4 µg.g -1 to 0.89 µg.g -1 ). The chromium concentration decreased from 2.2 to 0.85 µg.g -1 (61%), and Ni decreased from 22 to 2.6 µg.g -1 (88%). Most of the metals were therefore removed from the plastic debris, except Co, Cu, As and Sr. The drastic decrease in concentrations indicates that the metals were probably located on the sample surface: either 1) in the biofilm, 2) deposited as small mineral particles, or 3) sorbed to the plastic surface layers or onto precipitated mineral particles. Accumulation patterns needs to be better understood [START_REF] Rochman | Long-Term Sorption of Metals Is Similar among Plastic Types: Implications for Plastic Debris in Aquatic Environments[END_REF] .

Figure 2: Metal concentrations in mg.g -1 (left) and in µg.g -1 (right) for the PE mesoplastic MD0518-4 sample before (plain bars) and after washing with NaOH (diagonal stripes).

The EDX analysis of the particles collected by gently scrubbing the surface of the mesoplastic 14MD0518-4 showed the systematic presence of the following elements in the background: C, O, Na, Cl, K, and Ca (Figure SI3). However, we also observed the presence of particles with very heterogeneous compositions. There were relatively large particles that contained Ti, for example (Figure 3A). Nanometric particles containing Fe were also observed (approximately 10 nm, Figure SI4). Barium was also detected in some areas; the particles were a few 10s of nm in size (Figure SI5). Chromium and Au were detected as 10-nm particles (Figure SI6 and SI7). Some mineral particles of Cr, Fe, Ni, and Au were observed (Figure 3B). The TEM/EDX images highlighted that metals were located at the debris surface and sometimes as nanoparticles.

Relation between elements and samples: statistical overview

PCA was conducted in order to explore the origins of trace elements in plastics. Figure 4 presents the biplots of the variable correlations with respect to the first two principal components. Five independent factors with eigenvalues exceeding 1 were extracted, accounting for 75% of the total variance. Three of the factors, explaining 61% of the variance, were examined in this paper: the first PC accounts for 31.5% of the variance, the second for 19.0%, and the third for 10.4%. This value is the result of the high variability of the data owing to the heterogeneity of the samples and to the heterogeneity of the observations for Hierarchical clustering (Figure SI8) shows that the first sampling campaign strongly differed from the second and that the pellet and packaging samples differed. Examination of the dataset provides evidence that the elemental content of Ti was notably higher in the first campaign.

Considering the high elemental concentrations in mesoplastics, PC 1 was mainly attributed to the Ti, Ni, As and carbonyl indices. Cd and Zn showed loadings between 0.619 and 0.880. PC2 was related to Pb, Mo and Cr, with loadings ranging from 0.898 to 0.948. PC 3 was attributed to Ba and Al, with loadings of 0.852 and 0.822, respectively. The discussion of PCA focuses on the results for PC1, PC2 and PC3. The loadings for PC4 and PC5 were less than 0.630, and these PCs are not discussed.

M A N U S C R I P T A C C E P T E D

Minor metals, such as Fe and Mn, were correlated either with As (for Fe) or with Mo, Ti and Zn (for Mn). In all samples, Mn concentrations remained relatively low (<2 µg.g -1 ), while Fe concentrations reached their maximum in micro-and mesoplastics, averaging 25 µg.g -1 (average concentration among the data used for the PCA), which was higher than the values found in pellets and packaging materials. TEM/EDX observations revealed Fe nanoparticles on the weathered layers of the plastic debris; Fe was therefore mainly present here as precipitated minerals. Several studies have demonstrated that polymers and plastics can trap Fe particles [START_REF] Belzile | Insitu Collection of Diagenetic Iron and Manganese Oxyhydroxides from Natural Sediments[END_REF][START_REF] Fakih | A new tool for in situ monitoring of Fe-mobilization in soils[END_REF][START_REF] Pedrot | Interactions between natural organic matter, sulfur, arsenic and iron oxides in reoxidation compounds within riparian wetlands: NanoSIMS and X-ray adsorption spectroscopy evidences[END_REF] . SEM/EDS only provided evidence of nano sized minerals, some of them were identified as Fe oxides which are known to be strong adsorbent of As. Moreover, no As was detected by EDS, indicating that As concentration are too low to allow the precipitation of As as mineral. The correlation with As is explained by the high affinity of As for Feoxides. If any other metals are correlated with Fe and Mn, they cannot act significantly as a sorbent for other chemical elements. As a consequence, the results obtained here show that there is no significant trace metal sorption process on the Fe and Mn oxides present on the weathered layers of plastic debris. This was also observed with the second PCA, which was performed without the samples from the first campaign. The carbonyl index, which fingerprints the plastic degradation progress, was correlated with some chemical elements, such as As, Cd, Ni, Fe and Ti (the Spearman coefficients for the carbonyl index with As, Cd, Ni, Fe and Ti were 0.64, 0.51, 0.51, 0.58 and 0.50, respectively). These correlations provide evidence that degradation drives the increase in some chemical element concentrations on plastic debris. In contrast, a significant positive correlation also existed among Pb, Cr and Mo, which explains PC2. These 3 elements are commonly used together in formulations. Their presence in plastics can therefore be explained as residues of the plastic fabrication process.

PC3 displayed a positive correlation between Al and Ba (Spearman coefficient = 0.41): Ba is used in plastics as a stabilizer or pigment, and Al is used as a pigment. Several and Ba, suggesting that these elements may be used together in the formulation of plastics. However, we cannot exclude that Ba and Al precipitated on the weathered surface layer of the altered plastics debris, since both Ba and Al minerals were detected by TEM/EDX, PCA statistical analysis shows that the amounts of chemical elements are mainly a consequence of their use as additives in PE formulation. Their increase in meso-and microplastics compared with their values in packaging may be, for several of the elements, correlated to the carbonyl index (Figure 4); we can thus conclude that plastic degradation might be a significant factor in the increase in quantities of chemical elements.

In summary the analysis of plastic debris collected in the North Atlantic subtropical gyre revealed wide-ranging concentrations of metals. The metal concentrations followed the order of plastic debris > new packaging materials > preproduction pellets. The results demonstrated that there are two main source of metals on plastic debris are i) the additives initially incorporated in the plastic and ii) the metals resulting from adsorption and precipitation on plastics.

Conclusion

Plastic debris represents a potential source of toxic metals that can be released in the ocean through weathering but also represents a sink as a support for nanoparticles or trace elements.

However, further studies are needed to identify the exact mechanisms that account for the increase in metal concentrations on plastics debris (formation of new binding sites, use of weathering layers as a precipitation nucleus, etc.). This study demonstrates that the ingredients introduced in plastics, however, could present environmental and health concerns even if they are designed not to leach from the product during its use. Plastic debris can be a

  observations, including 10 mesoplastics, 22 packaging materials, 6 pellets and 4 microplastics), and the columns represent the variables, representing the elemental concentrations (15 elements, including Al, Ti, Cr, Mn, Ba, Fe, Co, Ni, Cu, Zn, As, Cd, Pb, and Mo) and their carbonyl index. PCA analyses were performed with R software (version 3.4.3).

Figure 1 :

 1 Figure 1: Trace metal element concentrations in virgin pellets (n=6), new packaging materials

Figure 3 :

 3 Figure 3: Observation by TEM (on the left) of particles extracted from the surface of the

  samples (namely, pellets, packaging materials, and meso-and microplastics).

Figure 4 :

 4 Figure 4: Principal component analysis results. (a) Biplot represents variable correlations with respect to the first two pincipal components (PC1 (31.45%) and PC2 (19.06%), total variance explained: 48%), and (b) Biplot represents variable correlations with respect to PC1 and PC3 (10.39%) for the whole dataset (pellets, packaging, and meso-and microplastics for all the sampling campaigns).

  yellow or purple plastic debris materials, presented high amounts of both Al

  metals for aquatic organisms as well as sea birds, and the associated risk should be further assessed.

Funding Sources

Part of the project is supported by the Total Corporate Foundation.

A C C E P T E D ACCEPTED MANUSCRIPT

Plastic debris collected from the North Atlantic subtropical gyre was analyzed for trace metals It is evidence that plastic oxidation favors the adsorption of some such as As, Ti, Ni, Cd

The presence of mineral particles on the surface of the plastic debris was evidenced by microscopy Plastics initially contain important amounts of metals resulting from intrinsic plastic additives Marine plastic debris carry complex mixtures of heavy metals

ACKNOWLEDGMENT

The authors thank the 7th Continent Expedition association for the sea sampling campaign; we warmly thank the staff and the crew. We also would like to acknowledge the technical teams of the ICPMS platform and clean room of the GET laboratory.


Miriam Gómez-Paccard
email: mgomezpaccard@csic.es

A Chauvin

M E Albeck

M A Zaburlín

D M Basso

F J Pavón-Carrasco

M L Osete

S A Campuzano

New archeointensity data from NW Argentina (1300-1500 AD)

A good characterization of the geomagnetic field strength at centennial to millennial time scales in the Southern Hemisphere is particularly crucial to disentangle the long-term evolution of the South Atlantic Anomaly (SAA), an intriguing geomagnetic feature currently observed at the Earth´s surface. Here we present 59 new archeointensities obtained from four well-dated groups of potteries with ages ranging between 1300 and 1500 AD and collected in Northwest Argentina.

The new data were obtained in accordance with the Thellier paleointensity method including partial thermoremanent magnetization (pTRM) checks and TRM anisotropy and cooling rate corrections. We have also performed a comparative study of the efficiency of magnetic the susceptibility, ARM and TRM anisotropy tensors to correct the TRM anisotropy effect upon intensity estimates. Our results suggest that the magnetic susceptibility tensor systematically underestimates the TRM anisotropy effect by 10 to 30 %. Our new data, together with a selection of selected archeointensities already published, confirm that the decrease of the geomagnetic field intensity in South America started around 1600-1650 AD, due to the arrival of the SAA.

Introduction

Paleomagnetic data suggest that the geomagnetic field, generated by a convective dynamo operating in the Earth´s electrically conducting fluid outer core, has existed for more than four billion years (Tarduno et al., 2015a). However, whereas changes of the geomagnetic field over the last centuries are rather well known thanks to historical and geomagnetic field observatory data [START_REF] Jackson | Four centuries of geomagnetic secular variation from historical records[END_REF], the uncertainties arising from hemispheric and temporal paleomagnetic sampling biases limit the robustness of geomagnetic field reconstructions for older times. Even the most recent Holocene geomagnetic field models provide significantly different patterns of temporal variation of the dipolar and non-dipolar terms of the Earth´s geomagnetic field (e.g. Pavón-Carrasco et al., 2014a;[START_REF] Constable | Persistent high paleosecular variation activity in southern hemisphere for at least 10000 years[END_REF]. As a consequence, the acquisition of new indirect observations derived from archeo-and paleomagnetic studies is crucial to disentangle the real variation of the geomagnetic field in the past. This requirement is particularly important for South America since this region contains one of the most intriguing features of the geomagnetic field at the Earth´s surface: the South Atlantic Anomaly (SAA).

The SAA is a region currently located at Southern Brazil where anomalous low field intensity values are observed due to the presence of a strong non-dipolar field. This anomaly has been commonly attributed to reversed flux patches at the core mantle boundary present below the South Atlantic. This feature is not only characteristic of the present field but also for the last 400 years [START_REF] Jackson | Four centuries of geomagnetic secular variation from historical records[END_REF][START_REF] Hartmann | New historical archeointensity data from Brazil: evidence for a large regional non-dipole field contribution over the past few centuries[END_REF]. Recent studies suggest that this anomaly is a persistent feature of the geomagnetic field for at least the last millennium but probably also for a longer time interval [START_REF] Tarduno | Antiquity of the South Atlantic Anomaly and evidence for top-down control on the geodynamo[END_REF][START_REF] Shah | Palaeomagnetic evidence for the persistence or recurrence of geomagnetic main field anomalies in the South Atlantic[END_REF]. Whatever the exact long-term trend, there is little doubt that the decrease of the axial dipole moment observed in historical times is related to the evolution of the reversed flux patches in the Southern Hemisphere and, hence, to the SAA history (e.g. [START_REF] Hulot | The magnetic field of planet Earth[END_REF][START_REF] Finlay | Gyre-driven decay of the Earth's magnetic dipole[END_REF][START_REF] Constable | Persistent high paleosecular variation activity in southern hemisphere for at least 10000 years[END_REF][START_REF] Pavón-Carrasco | The South Atlantic Anomaly: The Key for a Possible Geomagnetic Reversal[END_REF][START_REF] Terra-Nova | Relating the South Atlantic Anomaly and geomagnetic flux patches[END_REF]. A good knowledge of the geomagnetic field in the Southern Hemisphere is thus crucial to place into a long-term temporal perspective the rapid decay of the dipole moment observed nowadays.

In this context, new high-quality data from the Southern Hemisphere are clearly needed. Despite the substantial efforts made over recent years providing new paleomagnetic data for South America (Poletti et al., 2016 and references therein), our view of past geomagnetic field changes in this area is still rather vague. In this paper, we focus our effort to address this challenge and present new archeomagnetic data obtained from a set of pottery fragments collected in the Puna of Jujuy (NW Argentina). Our study also highlights that the magnetic susceptibility (and in some cases but in a lesser extent also the anhysteretic remanent magnetization) anisotropy tensors are not good proxies to correct the TRM anisotropy effect upon archeointensity estimates. As already pointed out before by [START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF], the vast majority of the previous published South American archeointensities should be discarded for geomagnetic field interpretation since they were not obtained following modern standards of quality. Finally, selected high-quality data from South America and global geomagnetic field reconstructions are used to discuss the origin and behavior of past regional intensity fluctuations in the area.

Methods

Modern humans colonized South America during the Pleistocene-Holocene transition. There is agreement that the human occupation of the Puna of Jujuy, the northern end of the Argentinean northwest, dates back to the early Holocene. The development of ancient hunter-gatherer societies continued for several millennia, with human groups subjected to marked climatic variations fluctuating between periods of high humidity (e.g. during the early-Holocene; [START_REF] Lupo | Los archivos paleoambientales en el Borde Oriental de la Puna y sus respuestas a los cambios naturales y antrópicos durante el Holoceno. Noroeste argentino[END_REF]Núñez et al., 1995-96;[START_REF] Schäbitz | Variaciones de la vegetación, el clima y presencia humana en los últimos 15.000 años en el Borde Oriental de la Puna, provincias de Jujuy y Salta[END_REF] followed by extreme dry events related to a global temperature increase (e.g. the Optimal Climate in boreal latitudes; [START_REF] Grosjean | Mid-Holocene Climate in the South-Central Andes: Humid or Dry?[END_REF]Núñez et al., 2013) that produced a sharp decrease of human occupation in the area. From ca. 3000 BP, climatic conditions comparable to those prevailing today favored the development of agro-pastoral societies of permanent residence. These societies developed advanced new technologies such as, for example, the manufacture of potteries. Subsequently, a growing complexity appeared, a process that culminated during the so-called Período Intermedio Tardío (Núñez, 2006), and dated from ca. 1000 AD to the annexation to the Inca Empire, about one century before the Spanish conquest in 1536 AD [START_REF] Tarragó | Chachas y Pukara. Desarrollos sociales tardíos[END_REF].

Here we present the study of four groups of pottery fragments (POPI, CASU, TUS2 and TUCI) collected in three different pre-hispanic agro-pastoral settlements of the highlands of the Jujuy province in northwest Argentina (Figure 1). The archeological sites are located near freshwater resources where remains of different human settlements were studied in the framework of different archeological investigations (Albeck and Zaburlin, 2008). Archeological evidence indicates that the four groups correspond to the Período Intermedio Tardío. Several radiocarbon analyses, calibrated using the IntCal 13 curve [START_REF] Reimer | IntCal13 and Marine13 Radiocarbon Age Calibration Curves 0-50,000 Years cal BP[END_REF], allow the establishment of an independent and more precise chronology for our studied collection, between the 14 th century AD and the first half of the 15 th century AD (Table 1).

%% Insert here Figure 1 %%

The collected potteries were analyzed in the paleomagnetic laboratory of Géosciences-Rennes, France. The fragments were cut into 2-4 specimens of about 1 cm, that were introduced in quartz cylinders. Remanent magnetization was measured using a 2G cryogenic magnetometer with a degausseur system, in a zero-field chamber. Anisotropy of magnetic susceptibility (AMS) was measured with a KLY3 Agico spinner kappabridge. Between two and four specimens per fragment were subjected to the classical Thellier experiment [START_REF] Thellier | Sur l'intensité du champ magnétique terrestre dans le passé historique et géologique[END_REF] following the same methodology explained in our previous studies (e.g. [START_REF] Gómez-Paccard | Archeomagnetic study of seven contemporaneous kilns from Murcia (Spain)[END_REF], 2012, 2016). The intensity of the laboratory field was fixed at 35 µT and the field was applied in the direction perpendicular to the plane of flattening of the studied potteries (z direction). Magnetic alteration was checked by performing pTRM checks every two temperature steps. The TRM anisotropy tensor was determined by measuring the laboratory TRM acquired following six different directions (-x, +x, -y, +y, -z and z in sample coordinates) at the temperature for which about 70% of the initial natural remanent magnetization (NRM) was lost.

At the same temperature, cooling rate dependence was estimated from three additional measurements, two corresponding to 1.5 h of cooling and one to 24 h (see [START_REF] Gómez-Paccard | Archeomagnetic study of seven contemporaneous kilns from Murcia (Spain)[END_REF] for further details). We assume an ancient cooling of about one day although no strong archeological constraints are available to determine the real cooling time of our potteries during their manufacture. The selection criteria used to check the reliability of the intensities obtained at the specimen level are similar to those applied in [START_REF] Gómez-Paccard | New constraints on the most significant paleointensity change in Western Europe over the last two millennia. A non-dipolar origin?[END_REF]. Only linear NRM-TRM diagrams corresponding to well defined straight lines going to the origin in the Zijderveld plots are considered. The differences between the original pTRM and the pTRM check must be lower than 10% of the total TRM. We fixed a limit of 50% for the fraction of the initial NRM involved in archeointensity determination (f parameter; [START_REF] Coe | Geomagnetic paleointensities from radiocarbon-dated lava flows on Hawaii and the question of the Pacific nondipole low[END_REF]. Finally, a maximum value of 5° was assigned to the maximum angular deviation (MAD; [START_REF] Kirschvink | The least-squares line and plane and the analysis of paleomagnetic data[END_REF] and the deviation angle (DANG; [START_REF] Pick | Holocene paleointensities: Thellier experiments on submarine basaltic glass from the East Pacific Rise[END_REF]. Furthermore, we also calculate the ratio of the standard deviation of the slope to the absolute value of the slope (ß) and the curvature parameter k [START_REF] Paterson | A simple test for the presence of multidomain behavior during paleointensity experiments[END_REF]. In addition, the MS, ARM and TRM anisotropy tensors TRM were determined on 18 selected specimens. Finally, different rock-magnetic measurements were carried out at the paleomagnetic laboratory of the Universidad Complutense de Madrid (Spain) on selected specimens. This includes susceptibility versus temperature curves, acquisition of isothermal remanent magnetization (IRM) and back-IRM curves, and hysteresis loops.

Results

Initial NRM intensities and bulk susceptibilities (χ) of the studied specimens define Königsberger ratios (calculated as Q = NRM/χ * H, with H = 39 Am -1 ) between 5 and 100.

These values indicate a good capability to maintain stable remanence and are typical of wellbaked argillaceous materials (e.g. [START_REF] Jordanova | On the suitability of baked clay for archaeomagnetic studies as deduced from detailed rock-magnetic studies[END_REF][START_REF] Catanzariti | New archaeomagnetic data recovered from the study of Roman and Visigothic remains from central Spain (3rd-7th centuries)[END_REF]. This suggests a thermomagnetic origin for the NRM acquired by our samples (Figure 2).

%% Insert here Figure 2 %%

Rock-magnetic results (Figure 3a-d) indicate that the magnetic mineralogy is dominated by magnetic grains characterized by low-coercivity phases and Curie Temperatures, estimated using the second derivative method of [START_REF] Tauxe | Paleomagnetic Principles and Practice[END_REF], in the range 510-580º C (except for two specimens), such as magnetites and titanomagnetites with low titanium contents.

%% Insert here Figure 3 %%

Thellier experiments were performed on 82 specimens. Fifty nine of them, corresponding to 31 different pottery fragments, have been retained to estimate past geomagnetic field intensities. They all respect the selection criteria detailed in the previous section (Figure 4a-e). The q values obtained for our retained specimens vary typically around 50, while and kare typically lower than 0.02 (see Table S1 and Figure S1 in the Supplementary Material online). The straight lines observed in the NRM-TRM diagrams and the generally low values of k (see Figure S1e) exclude a significant effect of multidomain grains behaviour. These (characteristic) components are interpreted as the TRM acquired during the manufacture of the potteries. The other specimens corresponding to a complex behaviour with two components of magnetization in the Zijderlveld plots (Figure 4f) were rejected.

%% Insert here Figure 4 %%

Our measurements show that the TRM anisotropy effect upon paleointensity estimates is very high for the majority of the studied specimens, with differences between the uncorrected and corrected intensity up to 50% (Figure 5 a-d). The cooling rate effect is generally low for the studied collection (Table S1). Our four mean archeointensities, calculated using the weighting approach proposed by [START_REF] Prévot | The Steens Mountain (Oregon) geomagnetic polarity transition. 2. Field intensity variations and discussion of reversal models[END_REF], indicate that during the 14 th and 15 th centuries AD field intensity in NW Argentina was between 41 and 49 T (Table 1).

%% Insert here Figure 5 %%

Discussion

Cautionary note on TRM anisotropy correction

Different effects, such as magnetic alteration, magnetic anisotropy or the presence of multidomain magnetic grains have not been systematically considered when geomagnetic field strength is estimated from paleomagnetic measurements. Together with dating uncertainties this fact produces important inconsistencies and internal discrepancies on both regional (e.g. [START_REF] Tema | Intensity of the Earth´s magnetic field in Greece during the last five millennia[END_REF][START_REF] Cai | Archaeointensity results spanning the past 6 kiloyears from eastern China and implications for extreme behaviors of the geomagnetic field[END_REF][START_REF] Shaar | Further evidence of the Levantine Iron Age geomagnetic anomaly from Georgian pottery[END_REF][START_REF] Molina-Cardín | Updated Iberian Archeomagnetic Catalogue : New Full Vector Paleosecular Variation Curve for the Last Three Millennia[END_REF] and global (e.g. Pavón-Carrasco et al., 2014a;[START_REF] Constable | Persistent high paleosecular variation activity in southern hemisphere for at least 10000 years[END_REF] data sets. Our results (see section 3) confirm that the correction of the TRM anisotropy effect is an essential requirement when paleointensity data are derived from highly anisotropic archeological objects, such as the potteries studied here. The TRM anisotropy is commonly interpreted as reflecting a preferential alignment of magnetic grains caused by a stretching of clay during the manufacturing process [START_REF] Aitken | Archaeomagnetic determination of the past geomagnetic intensity using ancient ceramics: Allowance for anisotropy[END_REF][START_REF] Genevey | ArcheoInt: An upgraded compilation of geomagnetic field intensity data for the past ten millennia and its application to the recovery of the past dipole moment[END_REF]. As a result, the easy planes of magnetization correspond to the planes of flattening of potteries as it is clearly observed in our data (see Figure 5c).

In the literature, the effect of magnetic anisotropy on paleointensity estimates has been evaluated using different approaches (see [START_REF] Genevey | ArcheoInt: An upgraded compilation of geomagnetic field intensity data for the past ten millennia and its application to the recovery of the past dipole moment[END_REF] for a review). The approach followed here is to determine the TRM anisotropy (ATRM) ellipsoids to directly correct both the NRM and the laboratory TRM at each temperature step [START_REF] Veitch | An investigation of the intensity of the geomagnetic field during roman times using magnetically anisotropic bricks and tiles[END_REF]. In some studies, the ATRM has been evaluated through the determination of the anisotropy of the anhysteretic remanent magnetization (AARM) or the anisotropy of the magnetic susceptibility (AMS), which were considered as proxies for the ATRM tensor. In order to investigate if these two approaches are valid on our samples, we obtained the AMS, the AARM, and ATRM ellipsoids of 18 fresh specimens following the procedure explained in Text S1 (Supplementary Information online).

The principal axes (K max , K int and K min ) of the different tensors and their corresponding orientations were obtained and plotted in Figure S2 (see also Table S2). The results show that even if the orientation of the principal axes of these tensors are very close, their shapes are different. They confirm the observations of previous studies performed on baked clays [START_REF] Chauvin | Paleointensity of the geomagnetic field recovered on archaeomagnetic sites from France[END_REF].

As a second test, we imparted a TRM to the 18 specimens by applying a magnetic field of 35 T perpendicular to the planes of flattening of the potteries and we then correct the direction and the intensity of these laboratory TRMs by using the MS, ARM and TRM anisotropy tensors (here and after AMS, AARM and ATRM tensors). For each specimen, we have compared the intensity of the laboratory TRM corrected by the AMS and ARM tensors with the one obtained using the TRM tensor. The results are plotted in Figure 5e. They indicate that when the AMS tensors are used the TRM intensity is systematically underestimated (with biases that can reach 30%). On the contrary, corrections performed with the ARM tensors can under-or over-estimate the TRM intensity, with biases ranging between 8 and -20%. These results suggest that archeointensity data corrected for the ATRM effect using the AMS tensor as a proxy are systematically biased toward lower values. Although important errors (around 20%) can also be introduced if the ARM tensor is used, in general the observed biases are lower than 10%.

As it is shown in Figure 5 a-b, the correction of the TRM anisotropy effect via the direct determination of the ATRM tensor is very effective since very similar intensity values are obtained after the ATRM correction. Moreover, the ATRM effect can be different on two specimens from the same pottery fragment (see also Table S1), as already suggested before (e.g. [START_REF] Genevey | ArcheoInt: An upgraded compilation of geomagnetic field intensity data for the past ten millennia and its application to the recovery of the past dipole moment[END_REF][START_REF] Gómez-Paccard | New archeointensity data from Spain and the geomagnetic dipole moment in western Europe over the past 2000 years[END_REF]. For this reason, we would like to highlight, as already pointed by [START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF], that mean fragment intensities cannot be calculated by averaging the estimations obtained from six sister specimens remagnetized following six orthogonal directions. This procedure was used in some previous studies (e.g. [START_REF] Morales | Magnetic properties and archeointensity determination on Pre-Columbian pottery from Chiapas, Mesoamerica[END_REF][START_REF] Goguitchaichvili | The archaeointensity of the Earth's magnetic field retrieved from Pampean ceramics (South America)[END_REF] but, it should be discarded in future studies.

The South American database

Modeling the geomagnetic field behavior over the past few millennia requires the use of both directional and intensity archeomagnetic data. While the acquisition of archeomagnetic directions is relatively straightforward, archeointensity determination is a laborious, complex and time-consuming task. As a consequence, there is a widespread lack of consistent coverage of archeointensity data in both time and space, with data gaps most pervasive in some areas. This is for example the case for South America, as already noticed by [START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF]. Although considerable efforts are being undertaken during the last few years (e.g. [START_REF] Roperch | Paleomagnetic study of juvenile basaltic-andesite clasts from Andean pyroclastic density current deposits[END_REF][START_REF] Roperch | Secular variation of the Earth's magnetic field and application to paleomagnetic dating of historical lava flows in Chile[END_REF][START_REF] Goguitchaichvili | Geomagnetic field intensity behavior in South America between 400 AD and 1800 AD: First archeointensity results from Argentina[END_REF][START_REF] Goguitchaichvili | The archaeointensity of the Earth's magnetic field retrieved from Pampean ceramics (South America)[END_REF][START_REF] Goguitchaichvili | Variations of the earth´s magnetic field strength in South America during the last two millennia: New results from historical buildings of Buenos Aires and reevaluation of regional data[END_REF][START_REF] Hartmann | Archeointensity in Northeast Brazil over the past five centuries[END_REF][START_REF] Hartmann | New historical archeointensity data from Brazil: evidence for a large regional non-dipole field contribution over the past few centuries[END_REF][START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF], only 225 mean archeointensities (about 7 % of the total database) are nowadays available for this region. This includes data from Ecuador, Bolivia, Peru, Brazil, Chile and Argentina with ages ranging between 3200 BC and 2000 AD (Table S3). The Virtual Axial Dipole Moments (VADM) derived from this dataset show very large dispersions (Figure S3). For example, VADM values between 5•10 22 and 18•10 22 Am 2 are observed around the 4-5 th centuries AD. This high dispersion is obviously related either to some low-quality data that do not fulfill modern standards of quality [START_REF] Genevey | ArcheoInt: An upgraded compilation of geomagnetic field intensity data for the past ten millennia and its application to the recovery of the past dipole moment[END_REF][START_REF] Gómez-Paccard | Improving our knowledge of rapid geomagnetic field intensity changes observed in Europe between 200 and 1400 AD[END_REF][START_REF] Paterson | A simple test for the presence of multidomain behavior during paleointensity experiments[END_REF] and/or to errors in the age determination of the studied archeological materials. The majority of the South American archeointensities have been derived from highly anisotropic objects (potsherds and ceramics) for which TRM anisotropy corrections were not performed at the specimen level or applied in an inefficient way (see also [START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF] for further details). However, we would like to note that, contrary to the global database, none of the previous archeointensity values available for South America have been derived from Thellier derived experiments corrected for the TRM anisotropy effect via the ARM anisotropy tensor determination. In addition, 57% of the available mean archeointensities were computed using less than four specimens. This, together with possible dating errors, easily explains part of the large dispersion observed in the South American dataset.

In order to disentangle the real geomagnetic field intensity trend in South America we reevaluate the South American dataset by applying a set of quality criteria similar to those explained in [START_REF] Pavón-Carrasco | Intensity of the geomagnetic field in Europe for the last 3 ka: Influence of data quality on geomagnetic field modeling[END_REF]. The selected dataset includes data derived from Thellier or Thellier-derived techniques including pTRM checks and ATRM corrections performed at the specimen level and using the TRM anisotropy tensor if potteries or ceramics were studied. Moreover, a minimum of 4 specimens to compute the mean intensity value is required. Finally, we only accepted intensity estimates obtained in a temperature interval comprising the ATRM correction. This procedure, although it may be somewhat restrictive, at least assures the reliability of the selected intensity values. The compiled and complete South American dataset together with the associated information used to classify the different data can be found in Table S3.

It appears that few archeointensities (43 in total) from South America can be considered as reliable mean intensities according to our criteria: 2 from Ecuador [START_REF] Bowles | Archaeomagnetic intensity results from California and Ecuador: evaluation of regional data[END_REF], 7 from Argentina (3 from Goguitchaichvili et al., 2011, and 4 from this study), 8 from Chile [START_REF] Roperch | Paleomagnetic study of juvenile basaltic-andesite clasts from Andean pyroclastic density current deposits[END_REF][START_REF] Roperch | Secular variation of the Earth's magnetic field and application to paleomagnetic dating of historical lava flows in Chile[END_REF] and 26 from Brazil [START_REF] Hartmann | Archeointensity in Northeast Brazil over the past five centuries[END_REF][START_REF] Hartmann | New historical archeointensity data from Brazil: evidence for a large regional non-dipole field contribution over the past few centuries[END_REF][START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF]. The ages corresponding to this high-quality compilation range between 3000 BC and 2000 AD, but all of them (except one data from Ecuador and two from Chile) correspond to the last 1000 years. We would like to highlight that our selected data set is almost identical to one compiled by [START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF]. This suggests that the paleomagnetic community is reaching some kind of consensus since our first claim for the need to perform a preselection of archeointensity data before geomagnetic field interpretation purposes [START_REF] Chauvin | Paleointensity of the geomagnetic field recovered on archaeomagnetic sites from France[END_REF], Gomez-Paccard, 2008).

Geomagnetic field intensity trends in South America over the last millennium

Archeointensity data from Argentina covering the last millennium are plotted in Figure 6. The low number of high-quality data for this region is clearly hampering a detailed analysis of past geomagnetic field variations over this period. However, our results together with previous selected data from Argentina suggest that the geomagnetic field intensity in this region was around two times higher than the present-day value until at least 1450-1500 AD (Figure 6).

According to selected archeointensity data, the geomagnetic field intensity decreased in Argentina during the 14 th century by ∼ 5 µT/century and increased during the 15 th century with a similar rate (Figure 6). The intensity predictions computed by recent global models based on archeomagnetic data (ARCH3k.1, [START_REF] Korte | The geomagnetic field for 0-3 ka, part II: a new series of time-varying global models[END_REF]and SHA.DIF.14k, Pavón-Carrasco et al., 2014a) show lower variations rates.

In order to have a general view of geomagnetic field intensity variation in South America, we have estimated the VADM corresponding to the available high-quality intensities (Figure 7).

They were compared with the dipole moment derived from the global geomagnetic models mentioned before as well as with the axial dipole evolution GMAG9k based on temporal and spatial averaging of the global intensity database [START_REF] Usoskin | Solar activity during the Holocene: the Hallstatt cycle and its consequence for grand minima and máxima[END_REF]. Our results confirm that the strongest discrepancies between observed VADMs and predicted dipole moments are observed after 1600-1650 AD (see also [START_REF] Hartmann | New historical archeointensity data from Brazil: evidence for a large regional non-dipole field contribution over the past few centuries[END_REF][START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF]. These discrepancies reflect both the decreasing of the dipole moment and the increasing contribution of non-dipolar sources, both phenomena contributing to the SAA.

%% Insert here Figure 7 %%

Conclusion

We obtained 59 new archeointensities for Northwest Argentina, obtained from the archeomagnetic study of four well-dated groups of potteries corresponding to the14 th and 15 th centuries. Our study demonstrates that the AMS (and in some cases also the AARM) tensor is not an efficient proxy to correct for the TRM anisotropy effect upon archeointensity estimates.

With this in mind, and in agreement with [START_REF] Poletti | Archeomagnetism of Jesuit Missions in South Brazil (1657-1706 AD) and assessment of the South American database[END_REF], we have selected the most reliable South American archeointensities. Our new archeointensity results together with selected data indicate that, over the last millennium, the geomagnetic field intensity in Argentina was around two times higher than the present-day value until 1450-1500 AD. Comparison with different global geomagnetic field reconstructions confirms that important differences between the VADMs and dipole moment trends occurred only after ∼1650 AD. Weaker dipole moment and stronger non-dipolar contributions explain the low field intensity values observed in South America due to the arrival of the SAA. The low number of high-quality archeointensities (and paleomagnetic directions) calls, however, for the acquisition of additional data for better constraining the dynamical behavior of geomagnetic field in South America. [START_REF] Dunlop | Theory and application of the Day plot (Mrs/Ms versus Hcr/Hc) 2. Application to data for rocks, sediments and soils[END_REF] and the SD + MD mixing curve also from [START_REF] Dunlop | Theory and application of the Day plot (Mrs/Ms versus Hcr/Hc) 2. Application to data for rocks, sediments and soils[END_REF] but based on data from [START_REF] Parry | Shape-related factors in the magnetization of immobilized magnetite particles[END_REF][START_REF] Parry | Magnetization of immobilized particle dispersions with two distinct particle sizes[END_REF].

FiguresFigure 1

 1 Figures

Figure 2 .

 2 Figure 2. Intensity of the Natural Remanent Magnetization (NRM) versus bulk susceptibility (10 - 3 SI). Lines indicate constant Köenigsberger ratios (Q).

Figure 3 .

 3 Figure 3. Rock magnetic properties. a) Normalized susceptibility versus temperature curves

Figure 4 .

 4 Figure 4. Representative examples of Thellier archeointensity experiments. Examples of accepted (a-e) and rejected (f) results. Temperatures are in degrees Celsius. Solid circles in the NRM-TRM plots indicate the temperature steps used for intensity determination. The NRM-TRM diagrams are shown together with Zijderveld plots, for which open (solid) circles indicateprojections on the vertical (horizontal) plane. The initial NRM, the q factor, parameter k (defined by[START_REF] Paterson | A simple test for the presence of multidomain behavior during paleointensity experiments[END_REF], and the intensity value before (F) and after (F TRM ) TRM anisotropy correction are indicated.

Figure 5 .

 5 Figure 5. a-b) Comparison of the NRM-TRM diagram obtained before (F) and after (F TRM ) TRM anisotropy correction for two sister specimens from the same pottery fragment. c) Stereographic projection, in sample coordinates, of the direction of the principal axis of the TRM ellipsoids for individual specimens of the four studied collections. K max , K int and K min are the principal, intermediate and minimum axes, respectively. The easy planes of magnetization correspond to the planes of flattening of the pottery fragments. d) Differences between TRM corrected (F TRM ) and uncorrected (F) estimates of geomagnetic field strength. e) Differences between the intensities of a laboratory TRM after correction using the TRM, MS and ARM tensors. The values (in %) are calculated as (TRM AMS or ARM -TRM TRM )/ TRM TRM .

Figure 7 .

 7 Figure 7. High-quality VADM values available for South America for the last 1000 years and comparison with different global dipole moment reconstructions (see text for furthers details).

Table 1 .

 1 Summary of the new archeointensities obtained for NW Argentina together with associated radiocarbon dates.Columns from left to right. Site, name of the archeological site where the samples were collected; Lab code, Laboratory code for the studied collection; Lat. and Long., geographical coordinates; 14 C sample and date, 14 C sample name and corresponding date in yr BP before

		VADM
	(%)	10 22 Am 2

Acknowledgements and Data Statement

This project was supported by the Ramón y Cajal program (RYC-2013-14405) and the CGL2015-63888-R (MINECO/FEDER) GEOMED project of the Spanish Ministry of Economy and Competitiveness. Financial support was also given by the PICS 2016 International Program for Scientific Cooperation (CNRS-France and CSIC-Spain). We warmly thank P. Roperch (Géosciences-Rennes) for his help with data treatment. AC would like to thank LP for comforting music. Data supporting this research can be found in the Supplementary Material online. In addition, the paleointensity measurement files are available at http://pc213fis.fis.ucm.es/mgomezpaccard/download.html. They have also been uploaded in the data repository Mendeley Data. Additional information upon request to mgomezpaccard@csic.es.

Supporting Information

Supporting information related to this article can be found online. [START_REF] Korte | The geomagnetic field for 0-3 ka, part II: a new series of time-varying global models[END_REF], SHA.DIF.14k (Pavón-Carrasco et al., 2014a) and CALS10k.2 [START_REF] Constable | Persistent high paleosecular variation activity in southern hemisphere for at least 10000 years[END_REF].

Archeointensity values and model predictions are referred to Pueblo Viejo de Tucute coordinates (23.03º S, 66.08º W). calibration; Calibrated age (AD), calibrated 14 C ages (AD); Final ascribed age (yrs AD), assigned age for each collection and associated standard deviation; N, number of independent fragments retained to calculate mean intensity; n, number of specimens retained to calculate mean intensity; Fm ± sd, mean intensity and standard deviation corrected for TRM anisotropy and cooling rate effect upon archeointensity estimates; σ b /F m , standard error/mean site field; VADM, values of the virtual axial dipole moment. The intensity values estimated at the specimen level together with the corresponding quality parameters are described in Table S1 (Supporting Information).

Highlights:

 TRM anisotropy correction is an essential requirement in paleointensity studies  Four new high-quality archeointensities are obtained for NW Argentina  The decrease of geomagnetic field intensity started in South America after 1600 AD


Georgios Balikas

Gaël Dias

Massih-Reza Amini

Houssam Akhmouch

Multi-task Learning for Semantic Relations Discovery

Keywords: Co-Hyponymy, Hypernymy, Multi-task Learning, Neural Networks

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Semantic relations embody a large number of symmetric and asymmetric linguistic phenomena such as co-hyponymy (bike ↔ scooter) or hypernymy (bike → tandem), and their automatic identification is of crucial importance for reasoning purposes. Most approaches focus on modeling a single semantic relation and consist in deciding whether a given relation r holds between a pair of words (x,y) or not (i.e. two-class problem). Another research direction consists in dealing with multiple semantic relations and can be defined as deciding which semantic relation r i (if any) holds between a pair of words (x, y) (i.e. multi-class problem). In this paper, we propose another way to look at the problem based on the idea that learning semantic relations concurrently may lead to performance improvements when compared to a set of two-class classifiers. Within this context, we propose to study both co-hyponymy and hypernymy based on the findings of [START_REF] Yu | Learning term embeddings for hypernymy identification[END_REF] that show that learning term embeddings that take into account co-hyponymy similarity improves supervised hypernymy identification. As a consequence, we define a multi-task learning strategy using a hard parameter sharing neural network model that takes as input a learning word pair (x, y) encoded as the concatenation 5 of both word embeddings. The intuition behind our experiment is that if the tasks are correlated, the neural network should improve its generalization ability by taking into account the shared information. Preliminary results over the gold standard dataset ROOT9 [START_REF] Santus | Nine features in a random forest to learn taxonomical semantic relations[END_REF] show that classification improvements can be obtained.

Methodology and Setups

Multi-task learning architecture. Concurrent learning of tasks that have cognitive similarities is often beneficial. We may hypothesize that recognizing related semantic relations concurrently can benefit classification models across tasks. To test this hypothesis, we propose to use a multi-task learning algorithm that relies on hard parameter sharing. The idea is that the shared parameters can benefit the performance of all tasks learned concurrently if the tasks are related. In particular, we propose an architecture based on a feed-forward neural network to perform the classification step illustrated in Figure 1. The input of the network is the concatenation of the word embeddings of the word pairs followed by a series of non-linear hidden layers. Then, a number of softmax layers gives the network predictions. Here, a softmax layer corresponds to a task, and concurrently learning M tasks requires M separate output softmax layers. The efficiency of hard parameter sharing architectures relies on the fact that the first layers that are shared are tuned by back-propagating the classification errors of every task. That way, the architecture uses the datasets of all tasks, instead of just one at a time. Learning setup. To concurrently learn co-hyponymy and hypernymy as two classification tasks, we implemented the multi-task architecture shown in Figure 1 using Keras [START_REF] Chollet | Keras[END_REF] and defined 2 fully-connected hidden layers (i.e. h 1 , h 2 , R = 2) of 50 neurons each as well as 2 softmax layers. The word embeddings are initialized with the 300dimensional representations of ConceptNet [START_REF] Speer | Conceptnet 5.5: An open multilingual graph of general knowledge[END_REF]. The activation function of the hidden layers is the sigmoid function and the weights of the layers are initialized with a uniform distribution scaled as described in [START_REF] Glorot | Understanding the difficulty of training deep feedforward neural networks[END_REF]. As for the learning process, we use the Root Mean Square Propagation (RMSprop) optimization method with learning rate set to 0.001 and the default value for ρ = 0.9. For every task, we use the binary crossentropy loss function and the network is trained with batches of 32 examples 6 .

Dataset and lexical split. In order to perform our experiments, we use the ROOT9 dataset [START_REF] Santus | Nine features in a random forest to learn taxonomical semantic relations[END_REF] that contains 9,600 word pairs. The word pairs are equally distributed among three classes (hypernymy, co-hyponymy and random) and involve adjectives, nouns and verbs. Here, we exclusively focus on nouns and keep all hypernyms, co-hyponyms and random pairs that can be represented by ConceptNet embeddings. Following a classical learning procedure, the dataset must be split into train, validation and test subtests. The standard procedure is random splitting. However, [START_REF] Levy | Do supervised distributional methods really learn lexical inference relations?[END_REF] point out that using distributional representations in the context of supervised learning tends to perform lexical memorization. In this case, the model mostly learns independent properties of single terms in pairs. To overcome this situation and prevent the model from overfitting, [START_REF] Levy | Do supervised distributional methods really learn lexical inference relations?[END_REF] suggest to split the train and test sets such that each one contains a distinct vocabulary. This procedure is called lexical split. Here, we propose to apply lexical split as defined in [START_REF] Levy | Do supervised distributional methods really learn lexical inference relations?[END_REF]. So, lexical repetition exists in the train and validation subsets, but the test set is exclusive in terms of vocabulary. Note that all subsets are available for replicability 7 .

Results

For comparative evaluation, we implement two baseline systems: (1) Majority Baseline and (2) Logistic Regression that has shown positive results in [START_REF] Santus | Hypernyms under siege: Linguistically-motivated artillery for hypernymy detection[END_REF] The multi-task paradigm shows that improved accuracy and MaF 1 scores can be achieved on average reaching respectively values of 86.8% and 82.6%, thus showing improvements of 0.9% and 3.1% over the best baseline (i.e. logistic regression). In this case, the best improvements are obtained for the classification of hypernym pairs with benefits of 2.3% in terms of accuracy and 5.5% in terms of MaF 1 , indeed suggesting that there exists a learning link between hypernymy and co-hyponymy. In parallel, the results for co-hyponymy classification are equivalent to a classical supervised strategy using logistic regression. So, the results seem to put in advance the fact that we can expect an improvement for hypernymy classification but not for co-hyponymy in a multi-task environment, suggesting a positive influence of co-hyponymy learning towards hypernymy but not the opposite. Note that these results seem to confirm the findings of [START_REF] Yu | Learning term embeddings for hypernymy identification[END_REF] for another learning scenario.

Conclusions and Future Directions

In this paper, we proposed to study the concurrent learning of co-hyponymy and hypernymy using a hard parameter sharing multi-task architecture and state-of-the-art distributional input representations (concatenation of ConceptNet embeddings). Obtained results show that concurrent learning can lead to improvements, thus justifying our initial hypothesis. In particular, we have shown that hypernymy classification can gain from concurrent learning of co-hyponymy. Based on these preliminary findings, a vast amount of improvements can now be introduced into the framework to increase overall performance. First, we aim at studying the interaction between more semantic relations such as synonymy and meronymy. Then, with respect to the input features, we intend to study the potential benefits from dedicated embeddings such as hypervec [START_REF] Nguyen | Hierarchical embeddings for hypernymy detection and directionality[END_REF] and dual embeddings [START_REF] Nalisnick | Improving document ranking with dual word embeddings[END_REF]. Moreover, we deeply believe that the LSTM path-based features introduced in [START_REF] Shwartz | Improving hypernymy detection with an integrated path-based and distributional method[END_REF] and some well-defined word pairs similarity measures [START_REF] Santus | Hypernyms under siege: Linguistically-motivated artillery for hypernymy detection[END_REF] can lead to classification improvements by complementing the information present in distributional semantic spaces. Moreover, it is clear that more complex architectures such as convolutional neural networks may improve the learning process as it is proposed in [START_REF] Attia | Cogalex-v shared task: Ghhh -detecting semantic relations via word embeddings[END_REF] for similar tasks. Finally, we plan to make the original task more difficult by including the detection of the direction of the asymmetric relations and adding noisy pairs as in [START_REF] Vylomova | Take and took, gaggle and goose, book and read: Evaluating the utility of vector differences for lexical relation learning[END_REF].

Fig. 1 .

 1 Fig. 1. Multi-task learning architecture.

Table 1 .

 1 over parts of the ROOT9 dataset. As for evaluation metrics, we report two measures: (1) Accuracy and (2) Macro-average F 1 measure (MaF 1 ). Accuracy captures the number of correct predictions over the total predictions, while MaF 1 evaluates how the model performs across the different relations as it averages the F 1 measures of each relation without weighting the number of examples in each case. Preliminary results of our architecture are illustrated in Table 1. ROOT9. ConceptNet embeddings. Accuracy and Macro F1 scores.

		Co-hyponym vs. Random Hypernym vs. Random Average Results
	Algorithm	Accuracy	MaF1	Accuracy	MaF1	Accuracy MaF1
	Majority Baseline	0.761	0.432	0.698	0.411	0.730 0.422
	Logistic Regression 0.900	0.841	0.818	0.748	0.859 0.795
	Multitask learning	0.895	0.849	0.841	0.803	0.868 0.826

Best configuration reported in[START_REF] Shwartz | Improving hypernymy detection with an integrated path-based and distributional method[END_REF] for standard non path-based supervised learning.

Code is available at https://github.com/balikasg/multitask-learning.

Data are available at https://github.com/balikasg/multitask-learning.