dataesr/scientific-paragraphs-categorization huggingface dataset:
It has largely been generated from a database with interpolation between soundings guided by satellitederived gravity data. From the data source, it is not clear what is the spacing between soundings in specific areas, like the area we analysed offshore Sumba, so we emphasize that these data should mainly be seen as first-order approximations of the bathymetry. The spatial resolution of BATNAS data is 6 arc-sec of latitude and longitude in relation to the mean sea-level datum ($180 m resolution around Sumba), whereas there are no robust estimates of the vertical uncertainty. We used these elevation data to systematically map the sequence of the reef terraces on slope maps and elevation maps. The landward limit of Quaternary reefal terraces on the large-scale map (Figure  2a ) was based on the morphologic contrast in roughness between the relatively smooth Quaternary terraces and relatively high-relief older formations and corroborated with the geological map (Figure  1 ).
ACKNOWLEDGEMENTS We thank the editor and two reviewers for their constructive and rapid comments and suggestions. We also thank  Kevin Pedoja  and  Anne-Morwenn Pastier  for discussions and advice. SYC acknowledges the  Program Insentif Riset Sistem Inovasi Nasional  research grant number  071/P/RPL-LIPI/INSINAS-I/II/I 2019 ,  National Geographic Explore  Grant to SYC number  CP 087R 17  for Sumba fieldwork,  Nusantara Research Grant  to RR number  341/E4. 1/AK. 04. PT/2021 , and to  Alexander von Humboldt Digital Cooperation fellowship  program to SYC. GDG acknowledges research permit  52/SIP/IV/ FR/6/2022  provided by the Indonesian government on 8 June 2022. GDG acknowledges postdoctoral funding from the  IRD  and the  Manajemen Talenta BRIN fellowship   program   2022.
Funding information Program Insentif Riset Sistem Inovasi Nasional  research grant, Grant/Award Number:  071/P/ RPL-LIPI/INSINAS-I/II/I 2019 ;  National Geographic Explore  Grant, Grant/Award Number:  CP 087R 17 ; Nusantara Research Grant, Grant/Award Number:  341/E4. 1/ AK. 04. PT/2021 ;  Alexander von Humboldt Digital Cooperation fellowship program ;  IRD ;  Manajemen Talenta BRIN fellowship  program.
The objective of our study is to determine whether using English tools to extract and normalize French medical concepts on translations provides comparable performance to French models trained on a set of annotated French clinical notes. We compare two methods: a method involving French language models and a method involving English language models. For the native French method, the Named Entity Recognition (NER) and normalization steps are performed separately. For the translated English method, after the rsttranslation step, we compare a twostep method and a terminology-oriented method that performs extraction and normalization at the same time. We used French, English and bilingual annotated datasets to evaluate all steps (NER, normalization and translation) of our algorithms. Concerning the results, the native French method performs better than the translated English one with a global f1 score of 0.
In this context, our work explores the question of the relevance of a translation step for the recognition and normalization of medical concepts in biomedical documents in French. We compare two methods: 1) a native French approach were only annotated documents and resources in French are used, and 2) a translation-based approach where documents are translated into English, in order to take advantage of existing tools and resources for this language which would allow to extract concepts mentioned in unseen French texts without new training data (zero-shot) as proposed in Van Mulligen et al. [5]. We evaluate and discuss the results on several French biomedical corpora, including a new set of 42 hospitalization reports annotated with 4 entity groups. We evaluate the normalization task at the documentlevel, in order to avoid a cross-lingual alignment step at evaluation time, which would add a potential level of error and thus would make the results more di cult to interpret (see word alignment in  [6, 7] ).
Background The different steps of our algorithms rely heavily on Transformers language models  [2]. These models are currently the state of the art for many natural language processing (NLP) tasks, such as machine translation, named entity recognition, classi cation, and text normalization (also known as "entity binding"). Once trained, these models can represent any speci c language, such as biomedical language or legal language. The power of these models comes from their neural architecture but also depends largely on the amount of data on which they are trained. In the biomedical domain, two main types of data are available: public articles (e. g. PubMed ) and clinical electronic medical records databases (e. g. MIMIC III  [14] ), and the most powerful models are for example BioBERT  [15]  which has been trained on the whole of PubMed in English, and ClinicalBERT [16] trained on PubMed and MIMIC III.
In French language, the variety of models is less important, with the models CamemBERT  [17]  and  FlauBERT [18]  for the general domain, and no speci c model available for the biomedical domain. In addition to the particularly powerful English pre-trained models, universal biomedical terminologies (i. e. , metathesaurus) also contain signi cantly more English terms than other languages. For example, the Uni ed Medical Language System (UMLS®  [1] ) contains at least ten times more English terms than French terms, which can enable ruled-based models to perform better in English. As mentioned above, each concept of reference in the UMLS®  [1]  is assigned a Concept Unique Identi er (CUI), associated with a set of synonyms eventually in several languages, and a semantic group -such as Disorders, Chemical and Drugs, Procedure, Anatomy and so on.
At the same time, machine translation has also gained in performance thanks to the same type of language models based on transformers, and the last few years have seen the emergence of high-quality automatic translation such as opus-mt developed by Tiedemann et al. [11] , Google Translate® and others. These last two observations led several research teams to add a translation step in order to analyze medical texts, for instance to extract relevant mentions in ultrasound reports  [19, 20]  or in the case of medical concept normalization  [9, 10, 21]. Work in the general (non-medical) domain has also focused on alignment between named entities in parallel bilingual texts  [22, 23].
Translated-English approach Axes 2. 1 and 2. 2 rst consist in a translation step, presented in section 3. 3 below, operated by the state-of-theart opus-mt-fr-en algorithm  [11]  or Google Translate®. Then, like axis 1, axis 2. 1 is based on a NER and a Normalization step. The NER step is done by the same algorithm but trained on the n2c2 2019 dataset  [24] , for the normalization step with used the same deep multilingual algorithm  [9]  (section 3. 5. 1) and the English version of CODER  [10]  based on a BioBERT  [15]  model. This axis allows to compare two methods whose difference is only the translation step. Axis 2. 2 is based on the MedCAT  [13]  algorithm presented in section 3. 5. 3 which performs the NER and normalization simultaneously. In this case, we compare the native French method with a state-of-the-art English system, ready to use, which is not available in French.
Overview For all our experiments, we chose to focus on only four UMLS®  [1]  group: Chemical & Drugs (CHEM) and Devices (DEVI) corresponding to medical devices such as pace-maker, catheter, etc. , Disorders (DISO) corresponding to all signs, symptoms, ndings (for instance positive or negative biological test results) and diseases, Procedures (PROC) correspond-ing to all diagnostic and therapeutic procedures such as imaging, biological tests, operative procedures, etc. Table  1  presents the datasets used for all our experiments with the corresponding numbers of documents. First, two French datasets were used for the nal evaluation, as well as for training the axis-1 models. QUAERO is a freely available corpus  [25]  based on pharmacological notes with two sub-corpora: Medline (PubMed abstract short sentences) and EMEA (drug notices).
We also annotated a new real-life clinical notes dataset from the Assistance-Publique Hôpitaux de Paris datawarehouse, described in Supplementary materials Section 1. 1. 1. Second, we used the corpus n2c2 2019  [24]  with annotated CUIs -on which we auto-matically added the UMLS ®  [1]  semantic group information, to train the axis-2. 1 system and to evaluate the English NER and normalization algorithms. We also used the Mantra dataset [26], corresponding to a multilingual gold standard corpus for biomedical concept recognition. Finally, we ne-tuned and tested the translation algorithms on both 2016  [27]  and 2019 [28] WMT biomedical corpora. Detailed description of the number of respective entities in the datasets can be found in the supplementary Table  1.
In our experiments, for the native French axis (axis 1 on Fig. 2 ), the pre-trained embeddings used to train the model were based on a FastText [36] trained from scratch on 5 Gigabytes of clinical text and a camemBERTlarge  [17]  ne-tuned on this same dataset. For the English axis 2. 1, the pre-trained models were BioWordVec  [12]  and clinicalBERT [16].
Normalization algorithms This step of our experiments is essential in order to compare a native French and a translated English method and consists in mapping each mention extracted from the text to its associated CUI in the UMLS®  [1]. We compare three models for this step, described below: the deep multilingual normalization algorithm developed by  [9] , the CODER  [10]  and the MedCAT model  [13]  that performs both NER and normalization at the same time. All these three models do not need any training dataset other than the UMLS®.
Translation performances For the two translation models, the respective BLEU scores  [38]  are computed on the 2016 Biomedical Translation shared task  [27]. A ne-tuned version of opus-mt-fr-en  [11]  on the 2016 and 2019 Biomedical Translation shared tasks was also tested. However, the Google translate model could not be used for our experiments involving clinical notes due to con dentiality reasons. Table  4  shows the BLEU score results for the three models, showing that ne-tuning on the opus-mt-fr-en model  [11]  on biomedical datasets led to the best results, with a BLEU score[38] of 0. 51. We will use this model for the overall performance of axes 2. 1 and 2. 2.
Discussion In this paper, we compared two approaches for extracting medical concepts from clinical notes. A French approach based on a French language model and a translated English approach where we compare two stateof-the-art English biomedical language models, after a translation step. The main advantages of our experiment are that it is reproducible, and that we were able to analyze the performance of each step of the algorithm: NER, normalization and translation, and to test several models for each step. 5. 1. 1. The quality of the translation is not su cient We show that the native French approach outperforms the two translated English approaches, even with a small French training dataset. This analysis con rms that, when possible, an annotated dataset improves feature extraction. The evaluation of each intermediate step allows us to show that the performance of each module is similar in French and in English.
We can then conclude that it is rather the translation phase itself that is of insu cient quality to allow the use of English as a proxy without loss of performance. This is con rmed by the performance calculations of the translation, where the calculated BLEU scores are relatively low, although improved by a ne-tuning step. In conclusion, although translation is commonly used for entity extraction or term normalization in languages other than English  [20, 40, 41, 42, 5] , due to the availability of turnkey models that do not require additional annotation by a clinician, we show that this induces a signi cant performance loss. Commercial API-based translation services could not be used for our task due to data privacy issues. However, the opus-mt model is considered state of the art, it is adjustable on domain speci c data, and the translation results presented in Table  4  con rm the lack of performance difference between this model and the google translate model.
Acknowledgments The authors would like to thank the  AP-HP  data warehouse, which provided the data and the computing power to carry out this study under good conditions. We wish to thank all the medical colleges, including internal medicine, rheumatology, dermatology, nephrology, pneumology, hepato-gastroenterology, hematology, endocrinology, gynecology, infectiology, cardiology, oncology, emergency and intensive care units, that gave their agreements for the use of the clinical data.
Data availability The datasets analyzed during the current study are not publicly available due the con dentiality of data from patient records, even after de-identi cation. However, access to the AP-HP data warehouse's raw data can be granted following the process described on its website: www. eds. aphp. fr, contacting the Ethical and Scienti c Commity at secretariat. cse@aphp. fr. A prior validation of the access by the local institutional review board is required. In the case of non-APHP researchers, the signature of a collaboration contract is moreover mandatory.
Data: Aggregate data derived from the IPD are shown for each trial below. Methods: We used these aggregate data to reconstruct the IPD, with a row for each participant in the dataset denoting their trial, treatment group, and outcome event. We then used maximum likelihood (ML) estimation to fit a one-stage logistic regression model to this IPD, with the intercept stratified by trial and assuming random treatment effects. This one-stage approach directly models the binomial likelihood, and so handles trials with zero events in one group. To improve ML estimation of the between-trial variance we used trial-specific centering of the treatment group variable (i. e. , 1/0 for treatment/control minus the proportion of participants in the treatment group for that trial). Confidence intervals were derived using the t-distribution with six degrees of freedom.
Acknowledgement This work is supported by the  ANR JCJC  project  THERMHYVAL  ( 2021-2024 ) 'Procédés thermochimiques hybrides pour la valorisation de chaleur fatale de parcs éco-industriels' and the project  PREMAT CNRS-INSIS  ( 2019-2021 ) 'Preuve de concept de procédé hybride multifonctionnel pour le stockage d'énergie, la production de froid et d'électricité'.
We introduce a semidefinite programming algorithm to find the minimal quantum Fisher information compatible with an arbitrary dataset of mean values. This certification task allows one to quantify the resource content of a quantum system for metrology applications without complete knowledge of the quantum state. We implement the algorithm to study quantum spin ensembles. We first focus on Dicke states, where our findings challenge and complement previous results in the literature. We then investigate states generated during the one-axis twisting dynamics, where in particular we find that the metrological power of the so-called multiheaded cat states can be certified using simple collective spin observables, such as fourth-order moments for small systems, and parity measurements for arbitrary system sizes.
Acknowledgments We thank  Rafał Demkowicz-Dobrzański ,  Janek Kołodyński ,  Géza Tóth  and  Jordi Tura  for useful discussions. IF acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie-Skłodowska-Curie  grant agreement No  101031549  (QuoMoDys). GMR, AKS, GRM, and ML acknowledge the support from:  ERC AdG NOQIA ;  Ministerio de Ciencia y Innovation Agencia Estatal de Investigaciones  ( PGC2018-097027-B-I00/10. 13039/501100011033 ,  CEX2019-000910-S/10. 13039/501100011033 , Plan National  FIDEUA PID2019-106901GB-I00 ,  FPI ,  QUAN-TERA MAQS PCI2019-111828-2 ,  QUANTERA   DYNAMITE PCI2022-132919 ,  Proyectos de I+D+I "Retos Colaboración "  QUSPIN RTC2019-007196-7 );  MICIIN  with funding from  European Union NextGenerationEU ( PRTR-C17.
I1 ) and by  Generalitat de Catalunya ;  Fundació Cellex ;  Fundació Mir-Puig ;  Generalitat de Catalunya (European Social Fund FEDER  and  CERCA program ,  AGAUR  Grant No. 2021 SGR 01452 ,  QuantumCAT U16-011424 , co-funded by  ERDF Operational Program of Catalonia 2014-2020 );  Barcelona Supercomputing Center MareNostrum  ( FI-2022-1-0042 );  EU  ( PASQuanS2. 1 ,  101113690 );  EU   Horizon 2020 FET-OPEN OPTOlogic  (Grant No  899794 );  EU   Horizon Europe Program  (Grant Agreement  101080086 -NeQST ),  National Science Centre, Poland  ( Symfonia  Grant No. 2016/20/W/ST4/00314 );  ICFO Internal  " QuantumGaudi " project;  European Union 's  Horizon 2020 research and innovation program  under the  Marie-Skłodowska-Curie  grant agreement No  101029393  ( STREDCH ) and No  847648  (" La Caixa" Junior Leaders fellowships   ID100010434 :  LCF/BQ/PI19/11690013 ,  LCF/BQ/PI20/11760031 ,  LCF/BQ/PR20/11770012 ,  LCF/BQ/PR21/11840013 ).
, 2012). The evolution of the displacement and the strain field during the experiments were measured using the free open-source Digital Image Correlation Engine (DICe) tool (https://github. com/dicengine/dice ) v2. 0 from Sandia National Laboratories  (Turner, 2015). Details on the methods and parameters used are provided in Table  1 , Appendix A, and as metadata in the Supplementary material. Raw image resolutions were 17. 26 μm/pixel (sample A) and 17. 98 μm/pixel (sample B); detailed image acquisition setup and procedures are described in the Supplementary material. The optimal subset size was set at 35 × 35 pixels using the autocorrelation approach  (Sutton et al. , 2009). For an optimal result, step and virtual strain gauge (VSG) sizes were set at 12 (~1/3 of the subset size) and 24 pixels, respectively  (International Digital Image Correlation Society et al.
4 mm thick) cut parallel and as close as possible to the observation surface using the Automatic Ice Texture Analyser (AITA), which provides an angular resolution of ~3°  (Peternell et al. , 2011; Wilson et al. , 2003). Pre-and post-deformation maps have spatial resolutions of 50 and 20 µm, respectively. Orientation data treatment, maps, and pole figures were made using the AITAToolbox (https://shorturl. at/krGS9). As Ice Ih deforms essentially by dislocation slip on the basal plane, the theoretical deformation capacity of a grain under axial compression can be approximated by the Schmid factor of the basal slip system using: 𝑚𝑚 𝑠𝑠 = 𝑠𝑠𝑠𝑠𝑠𝑠𝜙𝜙 𝑠𝑠𝑠𝑠𝑠𝑠𝜃𝜃 ( 1 -𝑠𝑠𝑠𝑠𝑠𝑠 2 𝜙𝜙 𝑠𝑠𝑠𝑠𝑠𝑠 2 𝜃𝜃) 1 2 (2) A Schmid factor of 0. 5 characterises grains with c-axes at 45° from the compression direction, resulting in maximum resolved shear stresses on the basal plane, while a value of zero implies c-axes parallel or perpendicular to the compression direction, leading to null resolved shear stresses on the basal plane.
Acknowledgements. This work was supported by a postdoctoral fellowship co-funded by the  European Union  and the  Government of the Principality of Asturias (Spain)  [grant number  ACA17-32 ] within the  Marie Skłodowska-Curie COFUND Actions FP7  to MALS and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation programme  [grant agreement No  882450 -ERC RhEoVOLUTION ]. We thank two anonymous reviewers for their comments that helped to focus and improve the manuscript.
Also, only one phosphorylated serine (Ser) was detected within AmelX, this last will be lost in a global phosphorylation calculation. Finally, the missing of unique AmelY spectra allow us to classify the remains as a female and, thus, as a Nun. Taken together, we show here that a simple peptidomics workflow is capable to validate antiquity based on physical properties of AmelX peptides in cases that deamidation values are corrupted and determinate sex. We believe our method provides a robust solution for the analysis of samples originating from sites with harsh environmental conditions or challenging collection scenarios for sample preservation. Data are available via ProteomeXchange with identifier PXD044694.
Discussion Our investigation centered around a distinct partial human dental specimen recovered from Khirbat el-Masani', a Christian monastery situated near Jerusalem, Israel, belonging to a Byzantine ascetic individual. The chained individual can be estimated as an adult, 30-60 years old. Our method shows the absence of the unique sequence commonly reported for male sex SM(ox)IRPPY  5, 6, 8  confirming that the tooth belonged to a FEMALE nun. To avoid false negatives, it is necessary to carry out further studies to broaden the database of unique peptides and confirm the reliability and consistency of this method across different populations and regions. This is particularly important in light of the reported AmelY deletion  32  cases. This study provides a simple methodology for characterization of the preservation state of AmelX by comparing site-specific modification and peptides length population distribution between modern and archaeological samples.
, 2000)  (Supporting Information S1). Since zooplankton is not collected within the WFD monitoring program, we considered fish and macroinvertebrate taxonomic data while assigning a single node for zooplanktons, phytoplanktons, protists, and bacteria (see below for more details). To infer the occurrence of trophic interactions among taxa, we used an allometric niche-based model relying on taxa occurrence and body size. We then analyzed how food web structure varies among lakes and depends on water temperature, productivity, and habitat characteristics. | 3 LECLERC et al.
| Body size estimates Once the biodiversity inventories were gathered for the 67 lakes, we estimated the taxa body size that is needed to run the allometric nichebased model. For fish species, average adult body size was obtained from different sources such as Fishbase  (Froese & Pauly, 2020)  and books on European fish biology (e. g. ,  Kottelat & Freyhof, 2007). For macroinvertebrate families, we inferred body size using an ecological trait database for freshwater macro-invertebrates that is fuzzycoded  (Usseglio-Polatera et al. , 2000). Fuzzy-coded affinity scores from the original database were transformed to obtain a quantitative estimation of the body size of each macroinvertebrate family (see Supporting Information S2 for more details). The four general taxonomic groups (i. e. , zooplankton, phytoplankton, protists, and bacteria) were characterized by a unique mean body size based on a literature survey  (Dussart, 1965; Laybourn-Parry, 1992).
, 2008). The relationships between composite food web descriptors (i. e. , PCA axes) and the different environmental variables were then investigated using generalized additive models (GAMs) that can handle nonlinear relationships  (Wood, 2017). To avoid overfitting, we constrained the GAMs at three knots. All models were fitted using the gam function of the R package "mgcv"  (Wood, 2017). Following  Braga et al. (2019) , we accounted for spatial dependency unexplained by the environmental drivers given that both the environmental variables and food web topological metrics can show some level of spatial autocorrelation. First, we accounted for the spatial dependency of food web structures by computing an autocovariate variable for each of the composite food web descriptors using the autocov_dist function of the R package "spdep"  (Dormann et al.