dataesr/scientific-paragraphs-categorization huggingface dataset:
Metal bioaccumulation in muscle For metal assay, muscle was freeze-dried, homogenized using Fastprep®-24 homogenizer and analyzed by inductively coupled plasma mass spectrometry (ICP-MS Agilent 7500ce) after hot acidic mineralization in HNO 3 (67e69%, Trace Metal Grade, Fisher Chemical). We assessed the reliability of the analyses using TORT-3 Table  1  Characteristics of the sampling site and sample sizes in each population and experimental treatment. Mean water Cd, Cu and Zn water concentrations over 4 years (corresponding to the life duration of gudgeon) were obtained from the Water Agency  (SIE database, 2016). The Toxic Unit (TU) was calculated as the logarithm of the sum of all-metal concentrations (Al, As, Cd, Cu, Co, Cr, Ni, Pb, Zn) on 4 years divided by the LC50 ratio for a model fish species (Pimephales promelas) and was used as a proxy of metal toxicity risk for fish  (Petitjean, 2019)  certified reference material.
Behavioral traits Fish behavior was analyzed using BORIS software  (Friard and Gamba, 2016)  four days after the immune challenge (AMIX injection), according to a previous study investigating behavioral effects of immune challenge in gudgeons  (Petitjean et al. , 2020). Three behavioral traits were selected because they are commonly affected by stressors: swimming activity, foraging and sociability  (Barber et al. , 2000; Jacquin et al. , 2017; Kirsten et al. , 2018; Koltes, 1985; Little and Finger, 1990 ) (see supporting material 6 for detailed method). Because behavioral traits were partly correlated, we used a PCA to extract three synthetic indices. The first behavioral index was named "General activity" (PCA axis 1, 41% of variance explained, supporting material 7). Fish with a higher general activity index on the first PCA axis swam for a longer time and across more areas.
Acknowledgements We thank anonymous reviewers for insightful comments and relevant corrections. The authors are grateful to  Emilie Rojas ,  Corinne Pautot ,  Sandrine Papillon , Michel le H enaff,  Flavien Garcia ,  Melissa Izard ,  Alexianne Damien ,  Leo Bottisagio ,  Pauline Madec ,  Camille Courcoul ,  Bruno Galle ,  Elisa Su Fu ,  Jessica Côte , and the technical service of  INP-ENSAT  for assistance at different stages of this study. We thank the  GET platform ,  Camille Duquenoy ,  Ga€ el Leroux  and  David Baqu  e for help with trace metal analyses. We are grateful to  Christel Lefrançois  and  Patrice Couture  for helpful comments on an earlier version of this manuscript. The EDB laboratory is part of the  French Laboratory of Excellence " TULIP " ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ).
Benthic indices For each station, we determined the most common and appropriate macrofauna biodiversity indices de ning the EQ of a given station or benthic community. In this study, some benthic species associated with hard fractions were identi ed but excluded from the statistical analysis. Collected data were used to calculate the species abundance (A, number of individuals estimated per m 2 ) to determine the most commonly used biodiversity indices for each station i. e. : the taxonomic richness (S, number of taxa 0. 2 m²), the Shannon-Weaver diversity index (H′) in log 2 (Shannon and Weaver 1963) and Pielou's evenness (J)  (Pielou 1966). Data analysis was carried out using version 6 of the PRIMER® (Plymouth Routines in Multivariate Ecological Research) software package  (Clarke and Gorley, 2006).
For the assessment of environmental quality, we make use of three biotic indices: AMBI (AZTI Marine Biotic Index,  Borja et al. 2000) , BENTIX  (Simboura and Zenetos 2002 ) and BO2A (Benthic Opportunistic Annelids Amphipods index;  Dauvin et al. 2016; Dauvin, 2018). AMBI and BENTIX were calculated using the software from AZTI, and then applied to estimate the proportions of ve ecological groups (using the species list published by the AZTI web site on 30 June 2019 http://ambi. azti. es/). These indices were used to qualify the ecological status on a ve-class scale of pollution, which considers ve ecological groups: EGI -taxa very sensitive to organic enrichment and disturbance, which are usually only present under unpolluted conditions; EGII -taxa indifferent to enrichment or disturbance; EGIII -taxa tolerant of excess OM enrichment, which may occur under normal conditions, but which are stimulated by organic enrichment; EGIV -second-order opportunistic species and EG-V -rst-order opportunistic species, able to resist strong disturbance.
05) was used for a posteriori multiple comparisons. Prior to the ANOVAs, analyses were carried out to test the normality (Kolmogorov-Smirnov) and verify the homogeneity of variances (Barlett). The structure of benthic communities between seasons (Se; 4 levels) and harbours sites (Si; 12 levels) was compared by a two-way permutational multivari ate analysis of variance (PERMANOVA). These statistical procedures were performed using R software.
Multivariate analysis The spatial distribution of the harbour macrofauna community was analysed by group-average sorting classi cation. This was followed by a cluster analysis and Non-metric Multidimensional Scaling (n-MDS) ordination of stations or groups of stations, based on the Bray-Curtis similarity measure and calculated from square-root transformed data. SIMilarity PERcentages (SIMPER) tests were performed to determine which species contributed most to within-group similarity. The signi cance of differences between the groups of samples so obtained was assessed by an Analysis of similarities (ANOSIM). The BIOENV procedure was used to identify which combination of environmental variables best explains the differences in macrofauna distribution. These multivariate analyses were applied by PRIMER ®-v6 software  (Clarke and Gorley, 2006 ).
Conclusion And Future Perspectives The present overview provides a valuable baseline database of benthic diversity in the GG harbours, covering a part of the central Mediterranean Sea that is still insu ciently studied. Different environmental parameters affect the distribution of benthic species. Many NIS are newly reported for the GG and have become established in the industrial ports owing to the continual increase of harbour activities. The ALEX metric shows that the ecological status of the GG harbours varies from unaffected to extremely affected by the rapid increase of NIS introduction. Changes in the metric according to the different sites show that AMBI, BENTIX, BO2A and ALEX respond to the range of environmental conditions found in each studied harbour (sediment type, harbour activity, pollution level).
To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters' GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively.
We show that the most important spectral bands explaining yield variability were green (0. 560 µm), red-edge (0. 726 µm), and near-infrared (NIR -0. 865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R 2 ) varying from 0. 21 to 0. 88 (averaging 0. 56) for the 30 m HLS and from 0. 09 to 0. 77 (averaging 0. 30) for 3 m Planet. R 2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.
Most studies used IKONOS, Quickbird, SPOT5, or the RapidEye satellites. Even through VHR, each would only provide one or a few image acquisitions throughout the season, which would bring large uncertainties in terms of optimal timing for correlating satellite-derived features with yields. Therefore, space-borne sensors at very high spatial and temporal resolution can provide new insights and opportunities for precision agriculture  [13, 24]  and further expand applications of remote sensing sensors in agricultural domains at various geographical scales. The present study takes advantage of VHR satellite data made available within the National Aeronautics and Space Administration's (NASA) program to explore the strengths and the limitations of multi-spectral sensors to explain crop yield variability at field scale.
• Q1: How does the spatial resolution affect our ability to explain crop yields at field scale? • Q2: To what degree can crop yield variability at field scale be explained by using space-borne multi-spectral sensors only? • Q3: What is the optimal timing of satellite image acquisitions and optimal spectral bands for explaining within-field variability? The study was performed for 2018 and 2019 using VHR data (1-3 m), acquired by PlanetScope (Dove-Classic) and WorldView-3 satellites. Additionally, use comparisons were made to moderate spatial resolution data (30 m) as acquired by Landsat-8 and Sentinel-2 satellites. The crops of corn and soybean were both analyzed with the study sites being within Iowa, US. The within-field ground refence data were precision collected by harvesting machines.
Study Area and Reference Yield Data Corn and soybean yields were obtained for 30 fields in 2018 and 2019 from Iowa State University. Fields were scattered throughout 7 counties in the state of Iowa, namely Boone, Buena Vista, Hamilton, Jasper, Lucas, Pottawatomie, and Story (Figure  1 ). The detailed characteristics of each field are described in Table  A1. The yield data were collected by combine harvesting machines, which provide dry volumetric yields roughly every three meters along track. These yields were pre-processed before correlating them to the satellite imagery. Headlands, row ends, and samples outside ±3 standard deviations from the field mean were removed. In cases when several data records with different yield values were within 3 m of each other, only the data record with the maximum yield was retained, while others were filtered out  [20, 25, 26].
The study was performed for 2018 and 2019 using VHR data (1-3 m), a PlanetScope (Dove-Classic) and WorldView-3 satellites. Additionally, use co were made to moderate spatial resolution data (30 m) as acquired by Landsattinel-2 satellites. The crops of corn and soybean were both analyzed with the being within Iowa, US. The within-field ground refence data were precision c harvesting machines. A1. The yield data wer by combine harvesting machines, which provide dry volumetric yields roug three meters along track. These yields were pre-processed before correlating t satellite imagery. Headlands, row ends, and samples outside ±3 standard devia the field mean were removed. In cases when several data records with differen ues were within 3 m of each other, only the data record with the maximum retained, while others were filtered out  [20, 25, 26].
We used an adapted version of the Land Surface Reflectance Code (LaSRC), previously developed for the moderate spatial resolution satellites such as Landsat 8  [28]  and Sentinel-2  [29]. LaSRC is a generic atmospheric correction algorithm for estimating land surface reflectance (SR), which takes into account absorption by atmospheric gases and scattering by molecules and aerosols. It is based on the inversion of the "6SV" radiative transfer code. The result of the atmospheric correction is the estimate of SR in the corresponding spectral bands of WV-3 satellite. Planet data, acquired by Dove-Classic sensors, were acquired through the NASA program  [30]. PlanetScope is composed of a constellation of satellites that provide a neardaily global coverage of the Earth surface  [31]. Dove-Classic sensors acquire data in the four spectral bands: blue (0.
485 µ m), green (0. 545 µ m), red (0. 630 µ m), and near-infrared (0. 820 µ m) at 3. 25 m spatial resolution. We acquired all cloud-free Planet data over the fields for the whole growing season in 2018 and 2019 and an SR product provided by Planet. There were 1167 and 1095 unique scenes, respectively, for those two years. We used NASA's Harmonized Landsat Sentinel-2 (HLS, ver. 1. 4) product at 30 m [32] to correlate satellite-derived features with crop yields. HLS is an analysis ready data product, which is derived from original TOA reflectance data by applying a series of pre-processing steps, including co-registration, cloud screening, atmospheric and angular corrections, and spectral adjustments. This ensures that data acquired by Landsat 8 and Sentinel-2 satellites are geometrically and radiometrically harmonized and can be used together.
Combination of Landsat 8 and Sentinel-2A/B satellites provides a global median average revisit interval of 2. 9 days  [33]  and already proved to be beneficial for crop yield assessment  [9]. HLS is organized using a Sentinel-2 tiling system with each tile covering the area Planet data, acquired by Dove-Classic sensors, were acquired through the NASA program  [30]. PlanetScope is composed of a constellation of satellites that provide a neardaily global coverage of the Earth surface  [31]. Dove-Classic sensors acquire data in the four spectral bands: blue (0. 485 µm), green (0. 545 µm), red (0. 630 µm), and near-infrared (0. 820 µm) at 3. 25 m spatial resolution. We acquired all cloud-free Planet data over the fields for the whole growing season in 2018 and 2019 and an SR product provided by Planet.
Satellite Data at Moderate Spatial Resolution: Landsat 8 and Sentinel-2 We used NASA's Harmonized Landsat Sentinel-2 (HLS, ver. 1. 4) product at 30 m  [32]  to correlate satellite-derived features with crop yields. HLS is an analysis ready data product, which is derived from original TOA reflectance data by applying a series of pre-processing steps, including co-registration, cloud screening, atmospheric and angular corrections, and spectral adjustments. This ensures that data acquired by Landsat 8 and Sentinel-2 satellites are geometrically and radiometrically harmonized and can be used together. Combination of Landsat 8 and Sentinel-2A/B satellites provides a global median average revisit interval of 2. 9 days  [33]  and already proved to be beneficial for crop yield assessment  [9]. HLS is organized using a Sentinel-2 tiling system with each tile covering the area of approximately 110 km × 110 km.
Methods First, we explored the effect spatial resolution has on capturing field-scale yield variability. For this, the harvest data from machinery were used to simulate how much yield variability is reduced when spatial resolution decreases. Yield data from the combine machinery were again available approximately every 3 m. Yield maps were generated at spatial resolutions of 10 m, 20 m, and 30 m by averaging all the point yield data within the coarser pixel. Relative efficiency, which is the ratio between yield variance at coarse resolution (10 m, 20 m, or 30 m) to yield variance at 3 m resolution, was used as a numerical metric for yield variability evaluation: R(x) = V y (x)/V y (x 0 ), (1) where R(x) is the relative efficiency at spatial resolution x m, V y (x) is the variance of yield at x m, and x 0 = 3 m.
Effect of Spatial Resolution on Capturing Field Scale Yield Variability Figure  5  shows the effect of spatial resolution on capturing within-field yield variability using a corn field as an example. These results show the difference between high spatial resolution data compared to moderate spatial resolution of 10-30 m. For example, only 59% of yield variability can be explained with 30 m, while the rest is lost because of coarsening and mixture effects inside the 30 m pixel. At present, one of the popular approaches for collecting reference crop yields is through the crop cuts  [14, 15]. At fine spatial scales (usually at less than 1 m), destructive harvesting of the crops is made, and the yield is estimated from the collected grain. Those yields are then used as reference and compared to satellite-derived yields at moderate spatial resolution.
When validating coarse (100 m-1 km) and moderate (10-30 m) resolution crop yield maps, one must ensure that variability inside the pixel is captured through multiple (10-15) point-based samples. In order to detect the optimal timing of correlating satellite-derived features with corn and soybean yields, a high temporal resolution of satellite data was required. This was achieved through the use of near-daily Planet data and a combination of 3-5 day revisit cycles of Sentinel-2 and Landsat 8 combined  [33]. The maximal correlations for corn and soybean were obtained at 3 and 2-2. 5 months, respectively, which is consistent with previous studies utilizing coarse resolution MODIS data and county-level yields  [36]. We observed a very variable performance of these datasets to explain within-field yield variability: coefficient of determination R 2 (Equations (  3 ) and (  4 )) varied from 0.
It means that multi-spectral data saturate over high yields and cannot capture yield variability. Other variables at such 3 to 30 m scale, such as soil moisture and evapotranspiration (ET)  [38] [39] [40] , irrigation vs. rainfed, should be considered, as previous studies showed that field-scale yield variability for corn and soybean in the US is mainly attributed to soil differences in the field and the water holding capacity  [41]. In terms of spectral bands, green (~0. 560 µm) and NIR (~0. 865 µm) were the most important for Planet and HLS to explain corn and soybean within-field yield variability. We did not consider red-edge from Sentinel-2, since it is not available in Landsat 8. When analyzing WV-3 data, red-edge (0. 726 µm) showed to be the most important in explaining yield variability.
Acknowledgments: We would like to thank  NASA  for providing support in generating the Harmonized Landsat Sentinel-2 (HLS) product. We would like to thank  Iowa State University  for providing field-scale yield data records. DigitalGlobe/Maxar data were provided by  NASA 's Commercial Archive Data for NASA investigators (cad4nasa. gsfc. nasa. govaccessed on 29 January 2021) under the  National Geospatial-Intelligence Agency 's NextView license agreement.
Exploring the role of changing climates in human evolution is currently impeded by a scarcity of climatic information at the same temporal scale as the human behaviors documented in archaeological sites. This is mainly caused by high uncertainties in the chronometric dates used to correlate long-term climatic records with archaeological deposits. One solution is to generate climatic data directly from archaeological materials representing human behavior. Here we use oxygen isotope measurements of Bos/Bison tooth enamel to reconstruct summer and winter temperatures in the Late Pleistocene when Neandertals were using the site of La Ferrassie. Our results indicate that, despite the generally cold conditions of the broader period and despite direct evidence for cold features in certain sediments at the site, Neandertals used the site predominantly when climatic conditions were mild, similar to conditions in modern day France.
This observation may help explain why exploring environmental influences on Paleolithic settlement patterns, behavior, or material culture in southwest France has so far been challenging, with different studies and sites yielding contradictory results  8, 11, 12, 14, 17. While factors such as dating uncertainty, heterogeneity in excavation methodology and assemblage curation are important, some disagreements may also stem from the use of broadly time-averaged geologically accumulated climate archives. In the case of La Ferrassie, we propose that the difference between proxies (geological data and faunal stable isotope data) suggests that Neandertal activity at the site was largely restricted to brief warm intervals of the generally cold MIS 4/late MIS 5a interval, indicating a particular site use pattern tied to mild climatic conditions.
Such archaeological data imperfectly capture underlying complex behaviors and are each driven by multiple interconnected factors. However, we suggest that a site-use pattern restricted to warm phases meant that La Ferrassie was particularly suited or attractive for warm phase occupation and may have provided a favorable environment for a specific suite of behaviors related to subsistence, site use, or material culture in Neandertal technology. A similar explanation has been suggested for the lower layers at Roc de Marsal where luminescence dates place the deposits in MIS 4 but with a more temperate faunal assemblage suggesting a wooded environment  62. Meanwhile, at other sits in the region, such as Pech de l' Azé IV, the upper layers of Roc de Marsal, Les Pradelles, Chez-Pinaud Jonzac and La Quina,  17  the presence of reindeer dominated faunal assemblages seems to suggest that Neandertal groups were nonetheless using the region more broadly during colder conditions.
Acknowledgements We thank  Birke Brumme  ( MPI-EVA ),  Elodie-Laure Jimenez  ( University of Aberdeen ),  Manuel Trost  ( MPI-EVA ),  Nicolas Bourgon  ( MPI-EVA ) and  Paulina Dittmann  ( MPI-EVA ) for practical support with sample preparation. We would also like to thank  Vaughan Grimes  ( Memorial University ) for conducting 87 Sr/ 86 Sr measurements, as well as  Sven Steinbrenner  ( MPI-EVA ) for technical support with carbon and nitrogen isotope measurements. Mael le  Corre (University of Aberdeen ) is thanked for generously providing the strontium isoscape figure in Supplementary Figure S12. Thanks are also due to  Aurélien Royer  ( Biogéosciences, Université de Bourgogne ) for helpful discussion. The excavations at  La Ferrassie  that produced the materials used in this study were led by  Alain Turq  with  Harold Dibble ,  Dennis Sandgathe ,  Paul Goldberg ,  Vera Aldeias ,  Laurent Chiotti , and  Shannon McPherron  with funding from the  National Science Foundation , the  Leakey Foundation ,  Le Conseil Général de Dordogne ,  Service Régional de L' Archéologie (SRA) ,  Institut National de la Recherche en Archéologie Préventive (INRAP) , the  University of Pennsylvania Museum , the  University of Pennsylvania Research Foundation , and the  Max-Planck-Society.
INTRODUCTION Understanding the composition and evolution of our Universe has been a central science endeavor in the astronomical community. On-going wide-field imaging surveys such as the Dark Energy Survey (DES 1  ,  Krause et al. 2017; Troxel et al. 2018a; Abbott et al. 2018 Abbott et al. , 2019)) , the Kilo-Degree Survey (KiDS 2  , van  Uitert et al. 2018; Kuijken et al. 2019; Hildebrandt et al. 2020) , and the Hyper Suprime Cam Subaru Strategic Program (HSC 3  ,  Mandelbaum et al. 2018; Hikage et al. 2019; Hamana et al. 2020 ) have collected a wealth of cosmological data over the past years that can be used to explore fundamental questions such as the underlying physics of cosmic acceleration, the mass and number of neutrino species, and the interplay of dark and luminous matter. The cosmological information is bound to increase significantly in the near future with analysis of the full DES, KiDS, and HSC datasets, and even more so in the early 2020s with the advent of Stage IV surveys such as the Vera C.