dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The distributions of earthworm data (density, total and mean individual earthworm biomass and diversity indices) were analyzed by Shapiro-Wilks to evaluate their fit to a normal distribution. In cases of deviation from normality, we found that the best fit models for data adjustment were Poisson for density and richness, and Gamma for biomass. Generalized Linear Models (GLiM) were used considering soil category (ADE and REF) and LUS (OF, YF and AS) as factors. Ancient human impacts were assessed by comparing the soil category, while modern human impacts were assessed by comparing the LUS (different levels of disturbance). When differences were significant (P < 0. 05), Tukey's test was used to determine differences among treatments using multcomp package in R software  (Hothorn et al.
Biodiversity indicators Mean earthworm species richness (mean number of species found), species richness within samples (number of species sample -1 ) and Shannon diversity index were calculated using standard formulae  (Magurran, 2004). Beta-diversity (β) indices were calculated using species occurrence and disregarding singletons (species represented by single individuals) to assess the species turnover components. Using Betapart package  (Baselga and Orme, 2012)  we calculated β Sørensen (βSør) dissimilarity index (max. diversity) and β Simpson (βSim) dissimilarity index (turnover) and Nestedness (βSør -βSim). β diversity values were partitioned according to the following factors: LUS (mean of beta-diversity indices obtained within a region in the same soil category); regional/spatial (obtained comparing the same LUS within each soil category); and soil category effect (result from comparisons between ADEs and REF soils in the same LUS within each region).
The Venn diagram was made using InteractiVenn  (Heberle et al. , 2015)  to show the distribution and overlap (shared) of the taxonomic units among LUS categories, LUS categories within the ADE and REF, and among the three regions (Central: Iranduba, Southwestern: Porto Velho, and Lower Brazilian Amazonia: Belterra) where the soil samples were collected. Species richness was compared by plotting rarefaction curves showing how species/morphospecies cumulated according to sampling intensity (as a function of the number of sampling units, i. e. the number soil monoliths). Rarefaction curves were adjusted globally for ADE and REF and for each LUS in each soil category using the iNEXT package  (Hsieh et al. , 2018). 2. 9. Soil physical and chemical attributes  Demetrio et al. (2021b)  provides data on soil physical and chemical properties (Sup.
Classification of the taxonomic units We obtained 257 COI sequences (i. e. 89 % sequencing success) which clustered into a total of 35 MOTUs (Fig. 2  and Sup. Table  4 ). The intra-MOTU divergence ranged from 0 to 4 % and inter-MOTU divergence ranged from 16 to 39 %, considering a genetic distance of 16 % as a threshold for separating interspecific MOTUs (Sup. Fig. 2 ). Using sequence data, we were able to delimit 19 MOTUs concordant with the morphological identification (IOTUs). Juvenile, tissue fragments and cocoons added a total of 16 MOTUs, representing 54 % of the total MOTUs and 37 % of the total taxonomic units. Additionally, 16 morphospecies were kept as unique taxonomic units because no DNA sequence was obtained from them. In conclusion, the integrative approach with both sequence data and morphological identification resulted in a total of 51 taxonomic units (IOTUs, MOTUs and morphospecies combined) among these, 1 singleton and 3 doubletons found in ADEs, and 11 doubletons found in REF sites.
Acknowledgments The study was supported by the  Newton Fund and Fundação Araucária  (grant Nos. 45166. 460. 32093. 02022015 , NE/N000323/1 ),  Natural Environment Research Council (NERC) UK  (grant No. NE/M017656/1 ), a  European Union Horizon 2020 Marie-Curie  fellowship to LC ( MSCA-IF-2014-GF-660378 ), and  DWGS  (No. 796877 ), by  CAPES  scholarships to WCD, ACC, TF, RFS, AF, LM, HSN, TS, AM, and  RSM  ( PVE A115/2013 ),  Araucaria Foundation  scholarships to LM, AS,  ACC , and ES, Post-doctoral fellowships to DWGS ( NERC grant   NE/M017656/1 ) and  ES (CNPq No. 150748/2014-0 ,  165702/2015-0 ),  PEER (Partnerships for Enhanced Engagement in Research Science Program) NAS/USAID  award number  AID-OAA-A-11-0001-project 3-188  to RMF and by  CNPq  grants, scholarships, and fellowships to ACF, GGB, RF, SWJ,  CRC , EGN, and PL (Nos.
Data pre-processing To analyse relevant variations in shape, the images were first segmented, and the 3D reconstructions (meshes) were globally aligned. In the following, we describe a semi-automatic pipeline for data preprocessing (illustrated in fig. 2 A ), similar as described in  Toussaint et al. (2021). The remaining manual inference consists of image segmentation correction (if necessary, e. g. , for poor quality images) and landmark placement for a global alignment of all shapes (10 landmarks per individuals). Image segmentation. An accurate segmentation of the mandible (image segmentation step in fig. 2  A. 2) is of utmost importance. One challenge of the data used in this study is its diversity. We considered individuals from different sources, as detailed in table 2. In most of the cases, the mandible has been individually scanned because dry skeletons were available (Strasbourg Collection, Pretoria Bone Collection and Dart Skeleton Collection).
Opening and closing are operators from mathematical morphology, a theory and technique for the processing of binary images. In some cases, a manual correction after the closing operation was necessary to, e. g. , separate the mandible in the CBCT scans of children. This was done using the opensource software ITK-snap (www. itksnap. org). Finally, we were able to obtain a clean segmentation of each mandibular symphysis, including the symphyseal area. We applied the same segmentation pipeline to each fossil specimen (without any manual correction necessary). The SK 63 specimens were scanned in two pieces which were aligned by manually selecting and aligning corresponding landmarks around the fracture (with a perfect fit). In the case of the KW 6420 and SK 63 specimens, the limited areas of bone loss caused by fractures (non-continuous surfaces across the symphysis) were filled manually using ITK-snap.
We employed deformetrica, a method and software previously described by  Durrleman et al. (2014)  to assess the complex differences in anatomical shapes. Shape variation is modelled by the deformation between shapes, rather than their positions (as is done in GMM by analyzing the position of landmarks). A detailed discussion on the modelling differences between GMM and DSM can be found in  Braga et al. (2019). A shape is represented as a triangulated mesh, consisting of an unordered set of points (vertices), connections between the points (edges), and closed sets of edges (faces). Two meshes are compared using the metric of currents  [Vaillant & Glaunes, 2005]  without assuming correspondences between the vertices. Instead, all vertices in a local neighborhood are considered, whose weights are dependent on a Gaussian kernel.
We created the template in ITK-snap and designed it to be as shapeless as possible. It has a vertical symphysis parallel to the axis and a U-shape in the sagittal orientation. Then, with an appropriate template, for a population of 𝑁 ! shapes, where 𝑁 ! is the number of shapes, 𝑁 ! registrations are performed. Employing pairwise registrations overcomes the difficulty of defining a template shape. However, instead of only 𝑁 ! registrations to construct an atlas, " ! (" ! $%) ' registrations are necessary to align all pairs. This makes the pairwise approach computationally more demanding. We used deformetrica 4. 0 with python 3. 8 and we ran all experiments on an Ubuntu workstation with 48 cores of 3. 80GHz with a GPU Quadro RTX 8000 48GB and CUDA 11. 1.
Acknowledgements This work was supported by the  French Ministry of Foreign Affairs (Commission des Fouilles) , the  Centre National de la Recherche Scientifique (CNRS) , and the " AESOP plus" consortium of Erasmus Mundus programme (Action 2 of the Education , Audiovisual and Culture Executive Agency of the European Union). We thank the curators of the  Institute of Anatomy in Strasbourg (France) , the  Pretoria Bone Collection (University of Pretoria, South Africa)  and the  Dart collection (University of the Witwatersrand, Johannesburg, South Africa)  for giving us access to mandibles representing modern human individuals. We also thank the  Evolutionary Studies Institute, University of the Witwatersrand  and the  South African Nuclear Energy Corporation (NECSA)  for access to the fossil specimens and help with micro-CT scans.
Introduction Clinical trial results shape clinical practice guidelines and support decisions that affect the health of millions of people. According to clinicaltrials. gov, ''in a clinical trial, participants receive specific interventions according to the research plan or protocol created by the investigators. These interventions may be medical products, such as drugs or devices, procedures, or changes to participants' behavior, such as diet. '' Randomization is a method used to randomly assign participants to the arms of a clinical study and is a key to ensuring the initial comparability of the groups. Following the Thalidomide crisis in 1962  [1] , randomization became a gold standard for clinical trials, to maximize their internal validity. Because their results must be trustworthy and resist the considerable financial and ideological conflicts of interest inherent in the evaluation of therapeutics, clinical randomized controlled trials (RCTs) have long been at the forefront of efforts to ensure transparency and reproducibility.
Search strategy and study selection The search for these registered reports used the Medline database with the following search string: ''registered report'' or ''IRRID''. Following peer review, we also performed a sensitivity analysis using the terms ''registered replication report*'' and ''preregistered report*''. In addition, we searched a list of published registered reports in a Zotero library compiled by the Center for Open Science https://bit. ly/2pJRYz3. Database searches were performed on December 17, 2021. All references identified were first automatically screened using R, excluding references where the title included the word ''protocol'' and/or did not include the character string ''random''. This procedure was validated on 100 randomly sampled references, retrieving 100% accuracy before being implemented (details of the validation procedure are shown in the Web Appendix 1).
3%) Psychology/Psychiatry 36 (38. 7%) 29 (37. 2%) 7 (46. 7%) Protocol availability 90 (96. 8%) 75 (96. 2%) 15 (100. 0%) Protocol published in a journal 90 (96. 8%) 75 (96. 2%) 15 (100. 0%) Protocol in the same journal/journal 35 (38. 9%) 33 (44. 0%) 2 (13. 3%) group b RCT presented on a register 89 (95. 7%) 74 (94. 9%) 15 (100. 0%) Name of the register c ClinicalTrials. gov 38 (42. 7%) 32 (43. 2%) 6 (40. 0%) ISRCTN registry 18 (20. 2%) 14 (18. 9%) 4 (26. 7%) ANZCTR 11 (12. 4%) 10 (13. 5%) 1 (6. 7%) German Clinical Trials Registry 7 (7. 9%) 6 (8. 1%) 1 (6. 7%) Chinese Clinical Trial Registry 6 (6. 7%) 4 (5. 4%) 2 (13. 3%) Netherlands Trial Register 5 (5. 6%) 5 (6. 8%) 0 (0. 0%) DRKS 1 (1. 1%) 1 (1. 4%) 0 (0. 0%) ICTRP platform 1 (1. 1%) 1 (1. 4%) 0 (0. 0%) Pan African Clinical Trials Registry 1 (1.
Interestingly, the authors also found some occurrences of the subjunctive in some of the two-year-old children. Based on this evidence, Parisse, Pontox, and Morgenstern  [76]  argue that at the age of 2 some verbal tenses can be evidenced, such as the present and the past, but also some moods, such as the imperative, conditional, and subjunctive (  [76]  (p. 154)). According to their research, at the age of 4, children already have good adult-like mastery of the subjunctive. These findings were not confirmed, however, by Bassano  [77] , who used an oral corpus of 2 French native children, aged 2-4 and 1-4 years, recorded longitudinally, and the crosssectional corpora of the so-called "TRL" database composed of recordings of children at 20, 30, 39, and 48 months of age (each age group consists of 20 children, with the exception of recordings of children at 20, 30, 39, and 48 months, each age group comprises 20 children, half of whom were followed longitudinally).
Linguistic proximity between French and Italian seems to perform an important role in French L2 acquisition of structures that are similar in both languages but not for the acquisition of the subjunctive, even if some functional aspects (e. g. , verbs expressing modalities, such as command, wish, suggestions, desires, etc. ) are similar in Italian and in French. The close typology relation thus appears to influence the acquisition of this mood for Italian learners of French L2. We will now discuss the role of the typological relation in the acquisition of the French subjunctive in more detail by reviewing selected psycholinguistic studies investigating Finnish, Turkish, Russian, and Anglophone learners of French. In Finnish, Paloheimo  [98]  evaluated the acquisition of Tense, Aspect, Mood (TAM) by Finnish learners of French from a database of written texts (TAITO).
Archeo-ichthyological and archaeo-malacological studies have increased in the last decades in the Mediterranean and cover almost all periods of Prehistory and Antiquity. Among other regions, the Aegean Sea offers a particularly diversiÞed spatiotemporal panorama of the early Mediterranean History, which highlights the diversity of responses vis-à-vis the marine environment, omnipresent in the Aegean landscape. The rich dataset from this region has allowed addressing a number of themes touching upon all aspects of livelihood by the sea. However, what has been addressed to a lesser extent is the role of diachronic variations of Aegean prehistoric Þsh landings, namely in the turn from Mesolithic to Neolithic and to Bronze Age communities, and the potential impact of ecological ßuctuations vs.
that of human choices on Þsh catches. This paper brießy explores this speciÞc research question by applying on a selection of Aegean prehistoric assemblages a methodological approach not commonly used in archeo-ichthyological research, namely a fundamental ecological model that seeks to assess how environmental and/or anthropogenic pressures impact the functioning of marine food webs and how societies adapt to these changes. This approach lies in the center of a newly launched ERC-CoG research program (MERMAID), that will improve and expand this exploratory application. Preliminary results show the potential of applying research questions and methodological tools from other disciplines in order to highlight tendencies and offer alternative interpretations that may add to the observations offered by standard approaches used in zooarchaeological research.
INTRODUCTION Aegean Prehistory affords us perhaps one of the richest records to understand the structure, operation, and evolution of the human-sea relationship in Mediterranean Prehistory. A growing and diversiÞed faunal dataset from this region, covering several categories of remains from marine organisms, namely Þsh, molluscs, crustaceans, corals, occasionally also remains of other invertebrates (octopuses or cuttleÞsh), allows to address a number of themes touching upon all aspects of livelihood by the sea: exploited Þshing grounds, year-round exploitation of marine resources, Þshing methods and tackle, marine foodstuffs in the prehistoric diet and cuisine, trade of marine products, the role of marine animals in ritual, status, beliefs, and artistic expression. However, what has been addressed to a lesser extent is the role of diachronic variations of Aegean prehistoric Þsh landings, namely in the turn from Mesolithic to Neolithic and to Bronze Age communities, and the potential impact of ecological ßuctuations vs.
that of human choices on Þsh catches. In this paper, I brießy explore this speciÞc research question by applying on the remains of Þsh from Aegean prehistoric sites a fundamental ecological model that seeks to assess how environmental and/or anthropogenic pressures impact the functioning of food webs and how societies adapt to these changes. This approach lies in the center of a newly launched ERC-CoG research program (MERMAID), that further expands this preliminary approach and which will also be brießy presented here.
Thus, a multi-factorial reassessment of the precise conditions and impacts of Þshing/collecting activities in earlier periods of the human history is needed. The above questioning is one of the research questions included in the ERC-CoG program MERMAID 1. The latter will explore available zooarchaeological records from this region, what could be described as "archaeo-Þsheries" archives, in order to follow variations through time and space. The project proposes to understand: a) how marine resources have been inßuenced by environmental/human pressures, b) when human impact can Þrst be identiÞed, and c) the ways ancient societies depended on these resources and adapted their exploitation strategies. With respect to the earlier periods in the Mediterranean, the project aspires to gain an insight into pristine conditions of marine ecosystems and study the environmental pressures but also potential Þrst impacts of human activities on natural stocks, by exploring the idea of shifting baselines, i.
3 The Aegean Mesolithic broadly spans between 10,000-6500 BC, the Neolithic between 6500-3200 BC, and the Bronze Age between 3200-1100 BC. | 178 | only some of these sites offer a large intra-site chronological sequence or statistically reliable samples from all strata. It is thus difÞcult to compare data for a same site through distinct chronological phases (see exceptions in the results). The selected dataset was deliberately chosen from a relatively limited marine zone, namely the North Aegean basin, coherent in hydrological and ecological terms, as biogeographical conditions in the northern parts of the Aegean Sea are distinct from those in the southern parts (the distinction between the two basins being south of the Pagasetic Gulf, between Euboea and the island of Psara,  Papaconstantinou 1988 p.
One of the ways to calculate the mean TL in modern Þsheries is by averaging trophic levels for the overall catch using the datasets for commercial Þsh landings. It is recommended that these estimations are combined with survey-based and model-based indicators, as they reßect more globally both the human and environmental impact on the entire marine ecosystem (e. g. Branch et al. 2010; Shannon et al. 2014 ). As stated above, the archaeofaunal material represents ancient Þsh landings. Thus, it does not directly provide data on general ecosystem composition, as it is biased by human selection. A method to remedy for this partial image is proposed by the MERMAID ERC program and is presented at the end of the paper, but was not applied on this previously conducted pilot study. The mean TL estimated for each studied period of the Aegean Prehistory was based on the represented identiÞed Þsh taxa from each archaeological sequence.
For each one, its TL value was attributed using data from the Fishbase database (Froese, Pauly 2022). Then, a formula proposed by  Reitz (2004) , adapted to zooarchaeological remains, was used (TL i = Σ (TL ij )(NISP ij ) / Σ NISP i , where i=speciÞc time period, j=each taxon, NISP=Number of IdentiÞable Specimens 5 ) in order to estimate the mean TL for each spatiotemporal group considered in this paper. The derived values were then compared with values from modern Aegean Þsheries. Before presenting the results of these estimations, a general evaluation of the prehistoric Þsh landings is useful.
The aforedescribed pattern of a Þshing activity that was not expanded enough to considerably impact Þsheries, might also be supported by a more traditional approach used in zooarchaeology, namely size reconstruction. Within the limits of this short study, only the two recurrent species in Aegean Prehistory, the gilthead sea bream and the grey mullet, were taken into account, as they offered reliable metrical and quantitative data, at least for the two periods considered here, the Neolithic and the Bronze Age. Reconstructed lengths of both studied species compared to modern data indicate the presence of bigger sizes of individuals (Þg. 5a-b) 15. This observation is interesting in [15] The addition of relevant data from the Mesolithic Cyclops would help complete the above general image, however given the already large individuals reconstructed for succeeding sites, it is not expected that sizes should indicate an inverse tendency.
Recent approaches suggest a combination of data, including survey-based and catchbased indicators, cross-checked with model-derived data. All the aforementioned render this type of record methodologically vulnerable. Despite these drawbacks, the Mediterranean offers a unique record of 'ancient catch data', as exempliÞed through the Aegean case study presented in this paper, which remains the only tangible archaeological record for some periods such as Prehistory. The archaeo-ichthyological record offers the possibility to not only reconstruct human-sea relationships from the earliest periods of human occupation in the basin, but also the resilience of both marine ecosystems and human societies through time. The newly launched ERC program MERMAID thus primarily relies on these ancient Þsh landings.
ACKNOWLEDGEMENTS Although the writing of this paper took place during the Þrst year of the ERC-CoG project mentioned in n. 1 (funding received from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program , Grant Agreement no. 01002721 ), the study itself was conducted before submitting the ERC proposal as an exploratory approach. No new data gathered/exploited within the aforementioned project have been added to this paper. Sincere thanks are addressed to  Prof. Konstantinos Stergiou  for stimulating discussions during initial phases of the writing. My gratitude extends to my two reviewers for valuable comments that signiÞcantly improved the presentation of the dataset and the arguments of the paper. The valuable work of  Peggy Bonnet  made the ßow of reviewing between them and I, possible.
X-Ray Diffraction (XRD) Corrosion products were analyzed by XRD using a RU-200B (Rigaku) rotating anode X-Ray generator equipped with a Mo anode (λ = 0. 7093 Å). The beam was focused on the sample with a beam size of 100 μm and a photon flux of about 2. 107 ph/s. XRD data were collected on a Pilatus 300K (Dectris) hybrid pixel detector in transmission mode with a 2Ө range from 2 to 35°. 2D images were circularly integrated using the FIT2D software and phase identification was carried out with Diffrac-EVA software (Bruker) integrating a reference database from ICDD. Between 25 to 35 mg of white powder were thermally decomposed at 400°C on a dedicated CO 2 collection line  (Beck et al. 2019; Messager et al. 2020; Dumoulin and Moreau, submitted). CO 2 was graphitized at 600°C using H 2 and iron catalyst  (Vogel et al.
ACKNOWLEDGMENTS The authors thank the LMC14 colleagues for their help during sample preparation, graphitization and AMS 14 C measurements, LSCE colleagues for IRMS δ 13 C measurements obtained on the Panoply analytical platform Dual Inlet mass spectrometer and  Eddy Foy  from  LAPA (Saclay ) for XRD experiment. We are also grateful to  C. Oberlin  from the  CDRC (Lyon ) for providing dated vinegar. This work was partially supported by the  Paris Seine Graduate School Humanities , Creation,  Heritage ,  Investissement d'Avenir   ANR-17-EURE-0021 - Foundation for Cultural Heritage Science.