dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Data was analyzed with TIBCO Statistica® (v. 14. 0, TIBCO Software Inc. , Palo Alto, USA), R software (v. 4. 0. 3, R Core Team, Vienna, Austria), and Microsoft® Excel® software (v. 16. 0. 12527. 21294, Microsoft Corporation, Redmond, USA). Correlation was assessed between gene expression (ΔΔCt values) and squared root of DS characteristics: visual score, relative area, intensity level, and shape. Gene expression in tissues was analyzed with ANOVA followed by Tukey test. SAV levels (Ct values) were compared with paired t-test. The significance level was set at p < 0. 05. The missing values (not detected) were set to 37.
Funding The study was supported by the Norwegian Seafood Research Fund (FHF) (901487), the Norwegian University of Life Sciences (NMBU), the Agence Nationale de la Recherche (ANR) (ANR-21-CE35-0019), and the Sechenov Institute of Evolutionary Physiology and Biochemistry of RAS (No. 075-00967-23-00). Fish material belonged to the Nofima's R&D licenses granted by the Norwegian Directorate of Fisheries for largescale Industrial Research.
Acknowledgments The authors acknowledge the skillful assistance and dedicated fish management of the technicians at Lerøy Midt AS, with special thanks to  Helge Endresen. The authors remark on the excellent work during sampling provided by  Dr. Jens-Erik Dessen  and  Håkon Torsvik. We wish to thank  Vibeke Voldvik  and  Marianne Hansen  for their help during RT-qPCR analyses and Ig sequencing.
g. ,  Brito, 2008; Winter et al. , 2016). However, these journals represent rigorous international conservation biology research  (Christie et al. , 2021). We checked the resulting publications for relevance (e. g. , a publication needed to be focused on amphibian conservation rather than simply mentioning it as an example) and saved titles, keywords (where present), and abstracts (or the first paragraph of the introduction when an abstract was missing) as a text file (Appendix S2). We used the R package tidyverse  (Wickham et al. , 2019)  in Program R (R Core Team, 2020) to extract and calculate the frequency with which words were used. We excluded common "stop" words (e. g. , "the", "and", and "to") and did not enumerate use of the keywords "amphibian/amphibians" and "conservation" as those terms were included in our initial search.
ACKNOWLEDGMENTS This manuscript is contribution #860 of the  USGS Amphibian Research and Monitoring Initiative (ARMI). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government. We thank all those who contributed research questions to this effort and to  B. R. Schmidt  whose comments improved the manuscript. JCI was supported by  MESTD RS  (Grants  451-03-68/2022-14/ 200124  and  451-03-68/2022-14/ 200007 ).
Freeze corer and Surber We calculated annual secondary production (g DM m -2 year -1 ) separately for the samples collected with the Freeze corer and the Surber. Our sampling was sufficiently replicated spatially but had a lower sampling frequency than other studies (e. g. ,  Walther & Whiles, 2011; Wallace et al. , 2015). To test the potential bias of a bimonthly compared to a monthly sampling, we conducted an additional analysis on a published dataset obtained in a stream similar to the studied here  (Wild, 2022)  (Online Resource 1). We found that secondary production achieved with a bimonthly sampling was, on average, 17% higher than the production achieved with a monthly sampling (Fig. 3 , Online Resource 1). However, overlapping confidence intervals showed that differences in sampling frequency were not significant.
Combined sample Given that the Surber may underestimate abundances of small-sized individuals and the Freeze corer may miss the larger individuals, we combined datasets obtained with both methods to serve as a reference for comparison of the sampling efficiency of the Surber and Freeze corer. In addition, we used the combined dataset to assess how varying mesh sizes impact secondary production. Specifically, we aimed to determine the degree to which changes in mesh size affect estimates of secondary production. Combining both datasets may lead to overestimating the abundances of the taxa (17 out of 25 taxa) collected with both methods. To minimize this potential bias, we inspected the body size distributions of each taxon obtained with both the Freeze corer and Surber sampler and identified overlaps between the two distributions.
Depending on the extent of overlap, various approaches were employed to reduce this potential bias (see Fig. 2  Decision tree to build the combined dataset, Online Resource 1). The methods were considered complementary if the 5th and 95th percentiles of the body length distributions of both methods did not overlap. Hence, all individuals captured with both techniques were incorporated into the combined dataset. Nevertheless, the body size distribution displayed partial overlap in most cases. In these cases, we assumed that the Freeze corer was better at collecting smaller individuals, while the Surber sampler was more efficient at capturing larger individuals. Therefore, we calculated the midpoint (m) of the overlapping length segment (Fig. 2c , Online Resource 1) and included all the individuals whose body length was less than m from the Freeze corer dataset and all the individuals whose body length was larger than m from the Surber sampler dataset.
Statistical analysis We evaluated differences in macroinvertebrate abundance and biomass between Freeze corer and Surber and sampling campaigns using a 2-way repeatedmeasures ANOVA (2 RM ANOVA). The data were checked for outliers with Grubbs' test, normality with Shapiro-Wilk's test, and sphericity using Mauchly's test, and log 10 transformed if needed. We reported the P-value after the Greenhouse-Geisser correction when the sphericity assumption was unmet. We performed pairwise comparisons with Bonferroni's method to test for differences due to seasonality in each sampling method. All those tests were performed in Origin (OriginPro, Version 2022. Origin-Lab Corporation, Northampton, MA, USA). To test for the main effect of the sampling method on compositional differences, we conducted a permutational ANOVA (PERMANOVA) with repeated measurements by using sampling campaign as a blocking factor (function adonis2).
Secondary production distribution among macroinvertebrate size classes and mesh efficiency in estimating production The contribution of the different size classes to production obtained from the combined dataset showed a right-skewed distribution with the main peak occurring between 1 mm and 2 mm (Fig. 5 ). Individuals with body length ≤ 2. 25 mm accounted for 50% of total production while individuals with body length ≤ 5 mm accounted for 79% of the total production (Fig. 5 ). The probability (unitless) of being retained by the 250 µm net was ≤ 0. 3 for individuals with a body length ≤ 2. 25 mm (Fig. 2 , Online Resource 3). It increased to 0. 9 for individuals with a body length ≥ 4. 25 mm and was higher than 0. 95 for individuals with a body length ≥ 5 mm. Reducing mesh size from 250 to 100 µm increased mesh efficiency by 36% (Table  1 ), underestimating 12% of the entire production.
Acknowledgements The authors thank  S. Bauth ,  S. Willige ,  A. Kneur ,  H. Matthes ,  K. Reinmann ,  R. Degenhardt  and  S. Jolitz-Seif  for their assistance with field and laboratory work. A. Hoff  and the  GEWANA department  for analysis of nutrients in the laboratory. D. Graeber  and  M. Weitere  provided helpful comments that improved earlier versions of the manuscript and  U. Scharfenberger  for assistance with the logistic model. Finally, we would like to thank the editor and two anonymous reviewers for their thoughtful comments and efforts to improve our manuscript.
Deployed on 24 selected headwater catchments from 12 to 141 km 2 located in north-western France, the method successfully matches the stream network in 80% of the cases and provides catchment-scale hydraulic conductivities between 9 x 10 -6 and 9 x 10 -5 m s -1 for shallow aquifers sedimentary and crystalline rocks. Results show a high sensitivity of 𝐾 to the density and extension of the low-order streams and limited impacts of the DEM resolution as long the DEM remains consistent with the stream network observations. With the emergence of global remote-sensing databases combining information of highresolution DEM and stream network, this approach will contribute to assess hydraulic properties of in shallow headwater aquifers.
, 2002; Anderson et al. , 2015; Huscroft et al. , 2018). Quantifying their contribution remains a challenge, as their hydraulic properties have classically been constrained only by sparse borehole-scale characterization, hydraulic tests, and head-based inverse problems  (Anderson et al. , 2015; Carrera et al. , 2005). As global databases compiling hydraulic conductivities give broad range of characteristic values by lithologies  (Gleeson et al. , 2014; Huscroft et al. , 2018; Hartmann and Moosdorf, 2012) , it has been shown that they cannot be applied directly to local catchments  (Reinecke et al. , 2019; Tashie et al. , 2021; de Graaf et al. , 2020). Analysis of stream discharge and borehole head dynamics have provided additional ways to estimate effective hydraulic properties at larger scales  (Brutsaert and Nieber, 1977; Vannier et al.
Topography and model geometry We first select the digital elevation model (DEM) that will be defined as the upper boundary of the groundwater model. In this study, we use the 75 m grid resolution DEM available at the scale of France. It is generated from photogrammetric restitution and provided by BD ALTI  (IGN, 2021). We also explore the impact of different DEM resolutions on the final estimations of 𝐾/𝑅. We consider two higher resolution DEMs of 5 m and 25 m also provided by BD ALTI. For coarser resolutions, the 25 m DEM was downsampled with nearest neighbour option to larger cell sizes, i. e. 100 m, 200 m, and 300 m. Geospatial processing is performed using the software WhiteBoxTools available in Python  (Lindsay, 2016) , labelled WBT in the following. All functions of WBT used are quoted in brackets in the following.
Groundwater flow model The MODFLOW software suite is used to solve the depth-integrated groundwater flow equation under steady state conditions (Eq. (  1 )) using a three-dimensional finite difference approach  (Harbaugh, 2005; Niswonger et al. , 2011) : 𝜕 𝜕𝑥 [ℎ 𝐾 𝑥𝑥 𝜕ℎ 𝜕𝑥 ] + 𝜕 𝜕𝑦 [ℎ 𝐾 𝑦𝑦 𝜕ℎ 𝜕𝑦 ] + 𝜕 𝜕𝑥 [ℎ 𝐾 𝑧𝑧 𝜕ℎ 𝜕𝑧 ] = 𝑊 (1) where ℎ [L] is the hydraulic head, 𝐾 [L T -1 ] is the hydraulic conductivity along the 𝑥, 𝑦, and 𝑧 coordinate axes, 𝑊 [T -1 ] is the volumetric flux per unit volume from the porous medium. We use the FloPy Python package  (Bakker et al. , 2016)  to set and handle simulations. To reduce uncertainties linked to potential flow across topographic boundaries, a buffer zone is added to the topographical catchment boundaries, increasing the modelled domain area by 10 %. The model domain is discretized using the regular mesh of the DEM.
Observation data of the stream network The observed stream network is extracted from the most precise hydrographic network database available for France, the BD TOPAGE, as a vector format at scale of 1:10 000 (IGN and OFB, 2019). The main vector file labelled "Cours d'eau" (Rivers) of the BD TOPAGE represents a majority of perennial sections of the stream network, i. e. filled and/or continuous-flow segments throughout the year. Note that the information classifying perennial or intermittent streams collected in the database is still under development to gain accuracy  (Schneider et al. , 2017). It has been rasterized at the grid resolution of the groundwater flow model to compare it to the simulated stream network (WBT. VectorLinesToRaster). Due to the uncertainty of the positioning of the stream network vector with respect to the DEM, the error is of the order of a pixel of the rasterization (plus or minus 75 m).
We have systematically tested the method using six hydrographic networks issued by different global, national, and local databases (Figure  7b1 ). These products display largely evolving densities coming from the origin, nature, and scale of observations (Appendix A). For 𝐷𝐸𝑀 𝑟𝑒𝑠 = 75 m, the criterion 𝑟 𝑜𝑝𝑡𝑖𝑚 (Eq. (  4 )) remains smaller than 2 for cases B to F qualifying them to estimate 𝐾 𝑜𝑝𝑡𝑖𝑚 (Figure  7b2 ). For case A only, the global-scale product HydroRIVERS  (Lehner et al. , 2013)  locates rivers away from the topographic valleys of the DEM issuing a 𝐷 𝑜𝑝𝑡𝑖𝑚 value more than ten times larger than 𝐷𝐸𝑀 𝑟𝑒𝑠. For cases B to F, the hydrographic network is well captured. Values of 𝐾 𝑜𝑝𝑡𝑖𝑚 vary over one order of magnitude from 7 x 10 -6 to 6 x 10 -5 m s -1. 𝐾 𝑜𝑝𝑡𝑖𝑚 decreases with more extended and denser hydrographic networks.
The nearest downslope flowpath distances (𝐷 𝑠𝑜 ̅̅̅̅̅ and 𝐷 𝑜𝑠 ̅̅̅̅̅ ) improve the Euclidean distance of the cell-by-cell and cellby-neighborhood analysis  (Franks et al. , 1998; Güntner et al. , 2004 ) by constraining the observed-to-simulated and simulatedto-observed stream networks to the topographical structures. The high sensitivity of the computed 𝐾/𝑅 with both the density and spatial extent of the observed stream network highlights the requirements to use high-quality observed stream products and, when necessary, to improve them. River maps available on national to global databases are often incomplete compared to local databases compiled by stakeholders on direct field observations leading to an overestimation of the effective hydraulic conductivities. Moreover, the results showed that the resolution of DEM should not be much higher than the resolution of the hydrographic network to ensure the consistency of the observed stream network with the DEM.
, 2019)  in ungauged catchments  (Sivapalan et al. , 2003; Hrachowitz et al. , 2013)  and to assess the impact of hillslope-and catchment-scale hydrology on global change predictions  (Taylor et al. , 2013). The methodology presented in this article is aimed to be deployed at multiple spatial scales taking advantages of global-scale databases compiling topographic, hydrologic, and climatic information. Such deployment would leverage the current innovations in remote sensing 335 and crowdsourcing  (Etter et al. , 2020 ) that now provide high resolution surface DEM products  (Hawker et al. , 2022; Yamazaki et al. , 2017)  and mapping of perennial stream networks  (Fovet et al. , 2021; Messager et al. , 2021; Grill et al. , 2019).
Acknowledgments This work was supported by " Eaux et Territoires " research chaire driven by the  Fondation Rennes 1. Clément Roques  acknowledges financial supports from the  Rennes Métropole research  chair " Ressource en Eau du Futur " and the  European  project  WATERLINE , project id  CHIST-ERA-19-CES-006. Alexandre Guavain acknowledges funding from the  RIVAGES Normands 2100  project. The investigations benefited from the support of OZCAR, H+. https://doi. org/10. 5194/hess-2022-175 Preprint. Discussion started: 16 May 2022 c Author(s) 2022. CC BY 4. 0  License.
https://doi. org/10. 5194/hess-2022-175 Preprint. Discussion started: 16 May 2022 c Author(s) 2022. CC BY 4. 0 License. vector format from 3 different sources: the global-scale database HydroRIVERS (cases A), the French database BD TOPAGE (cases B, C and D), and local scale inventories (cases E and F) performed within the framework of the SAGE (Schéma d'Aménagement et de Gestion des Eaux, in French). Compared to the BD TOPAGE database, the HydroRivers product is derived from a processing of DEM of lower resolution (approximately 500 m at the equator), while the local inventories are.
Earthquake locations in the 3-D model When calculated using the 1-D reference model, the locations of the four largest events obtained by the BCSF-R énass are related to some significant traveltime residuals at several stations, particularly for S -wave arrivals (Fig. S2 , Supporting Information). These residuals point to a significant difference in seismic wave velocity between the various stations, which may have an impact on the ear thquakes' inver ted locations. Due to the heterogeneous nature of the subsurface, we use the 3-D velocity model that was developed in the previous section to both better constrain the earthquake locations and minimize these effects. Using NonLinLoc software  (Lomax et al. 2001 ) , we invert the locations of the events based on the manual P and S wave arrival time picks made by BCSF-R énass operators.
Dir ecti vity anal ysis of the 2021 June earthquake rupture Schmittbuhl et al. ( 2021 ) identified a regional fault plane, included in the public database GEORG ( http://www. geopotenziale. org ) connecting the 2019 southern event and the northern cluster close to the wells, the so-called Robertsau fault. The direction of this fault varies between N025 • E and N010 • E from south to north (Fig. 2  ). This direction might match one of the nodal planes of the earthquake that occurred in 2019 November (Fig. 6  ). Ho wever , the seismicity distribution near the injection wells, as determined by double-difference relocation, reveals a preferred direction that mainly favours the auxiliary plane with N304 • E  (Schmittbuhl et al. 2021 ). A geomechanical stability analysis of the faults can benefit from knowing which of these two nodal planes is the real fault plane.
A C K N O W L E D G M E N T S We thank Zacharie Duputel and Luis Rivera for discussion on the analysis of the directivity. We thank Andrea Bruestle from LGRD for German's macroseismic data compilation. We thank the two anon ymous re vie wers and the editors that provided numerous comments that significantly improved the discussion between the source process and the observed intensities. This work was carried out in the framework of the Interdisciplinary Thematic Institute GeoT as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm. It was supported by ANR PrESENCE (ANR-21-CE05-0033-01), IdEx Unistra (ANR-10-IDEX-0002), and by SFRI-STRAT'US project (ANR ANR-20-SFRI-001) under the framework of the French Investments for the Future Program.
D ATA AVA I L A B I L I T Y All waveforms used in this study are available through the RESIF (RESIF 1995 ) and RASPBERRY-SHAKE (Raspberry Shake 2016 ) interfaces. The earthquake catalogue of the BCSF-R énass, waveforms and the 3-D velocity model built in this study are all available in a dedicated repository at https://cdgp. u-strasbg. fr. Some of the data processing is accomplished using Obspy software  (Krischer et al. 2015 )  and several figures have been made with GMT  (Wessel et al. 2013 ). Some figures also use map tiles by Stamen Design under CC BY 3. 0 and data by OpenStreetMap under ODbL.
Data and Methods Given the remoteness of the Kerguelen main island, most of its local earthquakes are recorded by a single seismic station: PAF (Port aux Français, Geoscope global seismological network), which is equipped with a Streckheisen STS-1 seismometer. The station started operating in 1983 and has produced continu-ous, 20 sample-per-second data streams since 1999. For this study, we used the three-component waveforms recorded at PAF between 7 January 1999 and 31 December 2021 (over 20 years of continuous data). The station stopped working completely between 2013/03/11 and 2013/09/16, during which time the original STS-1 electronics were upgraded to their Metrozet E300 successors. The East component STS-1 sensor malfunctioned during several months in 2017. Fortunately, a short period Mark-L4C sensor installed a few meters from the STS-1 instruments was operating during the 2017 malfunction, so we substituted waveforms from the L4C instrument in our analysis for that time period.
To identify earthquakes and automatically pick P and S-wave arrival times, we cut the continuous threecomponent data-streams into 24-hour windows and processed them using the EQTransformer algorithm of  Mousavi et al. (2020). This algorithm relies on a deep neural-network architecture both for earthquake identification and phase picking and has been trained on a worldwide database of local to regional waveforms. We kept the detection level threshold probability and the P and S picking probabilities at their default values, i. e. 0. 3, 0. 1, and 0. 1. The EQTransformer algorithm identified 6826 P -wave picks and 6864 S-wave picks in our data-set. As we were only interested in seismicity local to Kerguelen and required both a P and an S pick to locate the earthquakes, we retained only those events whose S pick followed its P -wave pick by less than 20s; this led to the identification of 6591 events.
We estimated the locations of these events from our single-station three-component data by combining epicentral (source-station) distances obtained from S-P travel-time differences with back-azimuths obtained from the direction of horizontal polarization of the Pwave arrival. We estimated the epicentral distances by matching the observed S-P travel-time differences with those computed in a 1D velocity model. Past geophysical campaigns in the region had found that the crust in the central region of the main island is thicker than normal oceanic crust (16-20 km) and that the crust-mantle boundary exhibits a 2-3 km thick transition zone  (Recq et al. , 1990 (Recq et al. , , 1994;; Charvis et al. , 1995). This depth of the Moho is compatible with the value of 24 km inferred from receiver function analysis at PAF  (Kumar et al.
Acknowledgments The authors thank  Frichnel W. Mamfoumbi  for her manual picking work which started this study. The authors also thank  Armelle Bernard ,  Jean-Yves Thoré , and  Maxime Bès De Berc  who, through their dedication to maintain PAF station, make it possible to coherently analyze seismological data spanning several decades. We also thank  A. Reading  and an anonymous reviewer for their numerous suggestions. The GEOSCOPE PAF station in Kerguelen Island is managed with support from the  French Polar Institute (IPEV)  under Program Number  133 "Global Seismological Observatory  ( SISMOLO-GIE / OBS) ".
Data and code availability All data from the GEOSCOPE PAF station are available through FDSN webservices on RESIF data center https://seismology. resif. fr/networks/#/G/PAF. All of the data recovery and some processing have been done using the Obspy python package  (Krischer et al. , 2015). Some figures have been created with the GMT software  (Wessel et al. , 2013). The catalog of located earthquakes is available on Zenodo  (Lengliné et al. , 2023).
This paper introduces a multi-perspective approach to deal with curation and exploration issues in historical newspapers. It has been implemented in the platform LACLICHEV (Latin American Climate Change Evolution platform). Exploring the history of climate change through digitalized newspapers published around two centuries ago introduces four challenges: (1) curating content for tracking entries describing meteorological events; (2) processing (digging into) colloquial language (and its geographic variations 5 ) for extracting meteorological events; (3) analyzing newspapers to discover meteorological patterns possibly associated with climate change; (4) designing tools for exploring the extracted content. LACLICHEV provides tools for curating, exploring, and analyzing historical newspaper articles, their description and location, and the vocabularies used for referring to meteorological events.