dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement Broadband seismological data are provided by the National Seismological Center (CSN) of the University of Chile through the Incorporated Research Institutions for Seismology Data Management Center (IRIS-DMC) under networks C1. The High rate GPS positions provided by Nevada Geodetic Laboratory, University of Nevada, Reno are available at http://geodesy. unr. edu/. Our catalog of earthquakes and repeating earthquakes  (Moutote et al. , 2023)  can be found at: https://zenodo. org/ record/8077490. Phase picking software EQTransformer  (Mousavi et al. , 2020b)  can be found at: https://github. com/smousavi05/ EQTransformer. Phase association software REAL  (Zhang et al. , 2019b)  can be found at: https://github. com/Dal-mzhang/REAL. The location software NonLinLoc  (Lomax, 2017)  can be found at: http://alomax.
The data are obtained from the French national service 'Banque Hydro'. The model grid covers the estuary from the mouth to the confluence of the Garonne and Dordogne rivers. However, tides influence the rivers farther, over ~90 km upstream of the confluence. In order to model the damping of the tides in the rivers, both tidal rivers are simulated by channels, in a 2DV representation (that is with just one cell in the transverse direction) as done for instance by  MacCready et al. (2009)  in the Columbia River or by  Piton et al. (2021)  and  Nguyen-Duy et al. (2021)  in the Red River delta with the Symphonie model. The Dordogne and Garonne discharges are then prescribed at the most upstream point of the channels (see  Nguyen-Duy et al. , 2021 ; their appendix B in supplementary material).
Data for model assessment Tides play a major role in the Bay of Biscay and Gironde estuary dynamics. They are then extensively validated, using a total of 52 tidal gauges for comparison of the amplitudes and phases of the main tidal constituents. Tidal harmonics calculated from satellite altimetry data are also used. Along-track amplitudes and phases are estimated from the sea surface height measurements of several satellites (TOPEX/Poseidon, Jason 1, Jason 2), through harmonic analysis  (Lyard et al. , 2021). We also compare model outputs with temperature and salinity data. We use the CORA-IBI database (temperature and salinity profiles) compiled by IFREMER  (Szekely et al. , 2017) , as well as in situ observations from the Islands network (IFREMER). A gridded (1 • × 1 • ) database giving monthly estimates of the SSS (sea surface salinity) in the Atlantic Ocean  (Reverdin et al.
Model assessment This section presents the results of model assessment for the REF simulation. 3. 1. Tidal elevations RMS complex errors are calculated for 6 tidal constituents (M2, S2, N2, K1, O1 and M4) and compared to the two datasets available: tidal gauges and satellite altimetry data (Table  1 ). 52 tidal gauges are used for this assessment. Errors are all given in cm and calculated with the following formula: H s = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ h 2 1 + h 2 2 √ h 1 = H m cos(G m ) -H o cos(G o ) h 2 = H m sin(G m ) -H o sin(G o ) Hs is the complex error, H m and G m are the modelled amplitude and phase respectively, and H o and G o are the observed amplitude and phase. Errors are calculated for each constituent. These errors are higher for tidal gauges than for satellite altimetry because gauges are mainly located in coastal areas where the tidal signal is stronger.
Temperature and salinity Comparisons of modelled SSS and SST to the Islands network buoys are summarized by the RMS errors presented in Table  2. The average error on SSS when compared to the gridded monthly SSS product in the Atlantic Ocean is estimated at 0. 18, for the years 2011 and 2012. Salinity and temperature profiles from the CORA-IBI database  (Szekely et al. , 2017)  were also compared to the model. Only profiles located between 44. 4 • N and 46. 5 • N and 3 • W and 0. 5 • W, corresponding to an area under the influence of the Gironde plume are taken into account in this comparison. Fig. 1b  shows the spatial distribution and number of profiles (log scale) used. In total for the years 2011 and 2012, 884 temperature profiles and 804 salinity profiles were used to calculate RMS errors (Fig.
Acknowledgements This work is a contribution to the  COCTO  and  COCTO-FO  projects ( SWOT Science Team Program ) funded by  CNES. Support from  CNES  and  CNRS/INSU  is gratefully acknowledged. This work was performed using HPC resources from  GENCI/CINES  (grants  2019-A0060110098  and  2020-A0080110098 ). The authors also thank  D. Gazen  ( Laero ) for the use of the Nuwa computing and storage facilities at  OMP, Toulouse, France. The SYMPHONIE code is distributed by the  French SIROCCO Service  (https://sirocco. obs-mip. fr/). Many thanks to  Puertos del Estado , the  SPC Gironde, REFMAR, Météo-France  and  IFREMER (CORA-IBI database, Islands network) ,  SHOM  and  OHI  for providing the in situ data. The authors gratefully acknowledge the receipt of SSS gridded data from  E. Kestenare  ( LEGOS ) and of ASPEX currents data from  G.
Charria ,  S. Theetten ,  L. Marié  and  A. Le Boyer  ( IFREMER ). Many thanks to  F. Lyard  ( LEGOS ) for discussion about tides and for providing us altimetric tidal constituents and to  Pierre De Mey-Frémaux  for discussions on the manuscript. We also thank  T. Duhaut  and  C. Nguyen  for their valuable help with the model grid and SYMPHONIE initial setting. Maps and model-data comparisons for tidal elevations have been partly made using the tools and POCViP software, developed at LEGOS by  D. Allain  and  F. Lyard. This study has been conducted using ECMWF products (www. ecmwf. int) and E. U. Copernicus  Marine Service Information  (product: DATASET- IBI-REANALYSIS-PHYS-005-002 -DAILY, produced by  Puertos del Estado  and  Mercator-Ocean , and available at https://re sources. marine. copernicus.
2009 )  and Venus  (Dav e y et al. 2013  ). Ho we ver, this work will mostly focus on Martian pits for their prominence in Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experiment (HiRISE) satellite imagery, as well as their role as potential entrances to underground caves. Martian pit craters were first proposed as potential cave entrances due to the thermal properties of their interiors resembling that which would be expected of subsurface material  (Cushing et al. 2007 ). Using Mars Odyssey Thermal Emission Imaging System (THEMIS) infrared imagery, the candidate's interiors were found to emit these wavelengths more strongly than the exposed surrounding surface during the night (see  Cushing et al. 2007 , fig. 4 ). Since then, MRO Context Camera (CTX) and HiRISE images have been surveyed to produce a data base of possible cave entrances on Mars called the Mars global cave candidate catalogue (MGC 3 ; Cushing 2015 ).
F or e xample, of the 1062 MGC 3 features, only eight were contained within an MRO HiRISE DTM. A method for estimating the depths of pits that does not require such ele v ation data is to observe the size of the pit's shadow as it appears in visual satellite imagery. The principle is that a deeper pit will cast a wider shadow than a shallower one, assuming that the Sun is in the same relative position for both features. Wyrick et al. ( 2004 )  propose a method for measuring the width of a pit's shadow ( S ) as a means of calculating its apparent depth ( h ) -the relative depth at the edge of the shadow -and estimating their volumes by assuming all pits are conical. According to the MGC 3 documentation, a similar approach, as outlined in  Cushing et al. ( 2015 ) , is also 'generally' used to provide estimates for h for all MGC 3 APCs (Cushing 2015 ).
In addition to this, Machine Learning (ML) and Deep Learning (DL) are being used to automatically detect planetary surface features in remote-sensing imagery  (Wang et al. 2017 ; Tertius Bickel et al. 2020 ; Lagain et al. 2021 ; Rajaneesh et al. 2022 ) , including work on detecting Martian pits  (Nodjoumi, Pozzobon & Rossi 2021 ). These techniques will serve to bolster existing catalogues and increase their sizes. Therefore, a method of automatically estimating pit depths from visual satellite imagery will be critical. In this work, we present a publicly available tool that can automatically calculate apparent depths of Martian and Lunar pits by extracting their shadows and measuring the widths. This application, named Pit Topography from Shadows (PITS), is a Python-based framework that only requires a single cropped remote-sensing image to operate.
These images were acquired by finding the MGC 3 APCs that had corresponding HiRISE imagery and selecting the ones which exhibited good variation in size and morphology. MGC 3 APCs were chosen since it was one of the most abundant classes and appeared the most visually synonymous with the definition of a pit. The corresponding pits were also contained in the colour versions of 12 of these 19 red-band images, allowing for comparison between the different data types. These testing images were manually labelled using polygons in QGIS (QGIS Development Team 2022 ) to produce georeferenced ESRI shapefiles for validation. The polygons in these 'validation shapefiles' were assigned an attribute field, called 'class', which described what the particular region of pixels represented. A class of 1 was assigned to the largest continuous shadow in the image, which had clearly been cast by the pit's rim.
Automated shadow extraction As discussed in Section 1 ,  Wyrick et al. ( 2004 )  provide a method for estimating the depth of a pit by manually measuring the width of the shadow along the Sun's line of sight in GIS software. In this work, we choose to employ image segmentation to automate this process, whereby a cropped satellite image of a pit would be separated into pixels that contain shadow, and those that are nonshadow background. This would result in a binary shadow mask consisting of detected shadow and background pixels (assigned a 1 and 0, respectively). Image segmentation methods can range in complexity, from simple thresholding, all the way up to trained semantic/instance segmentation ML or DL models. As mentioned previously, the aim when developing PITS was to devise a method that reflected the simplicity for a human to delineate a shadow.
Automatically measuring shadow widths The task of manually measuring a shadow's width along the Sun's line of sight is relatively straightforward thanks to map-projected satellite data and GIS software tools. On the other hand, making the same measurement automatically upon the binary shadow masks (extracted via the methodologies in Section 3 ) is not as simple. This is due to the fact that the Sun's line of sight can come from a range of angles. In order to account for this, PITS utilizes the solar azimuth angle ( ϕ), which is regularly given in the metadata for planetary remotesensing imagery. ϕ is the clockwise angle between some reference point (due north in PITS' case) and the subsolar point -the location directly below the Sun on the surface. Fig. 5  shows how the observed shadow width ( S obs ), as seen by the HiRISE camera, can be measured by knowing ϕ.
e. image resolution, α, ϕ, ε, and ω). The h profiles derived from the 11 HiRISE images of 10 APCs that suggest possible cave entrances are also given in the online supporting material, along with the detected shadows as georeferenced shapefiles. The PITS tool itself is publicly available through GitHub ( ht tps://github. com/dlecorre387/Pit -Topography-fr om-Shadows ), where there is an installation guide and a user tutorial. RASTAI 2, 492-509 (  2023 ) ε ࢱ can be found by knowing the range of possible ε v alues gi ven the size of the image and using equation ( A2 ). ε = ε ∂ε ∂ε = ε ∂ ∂ε arctan ( v/d h ) = ε ∂ ∂ε arctan | cos γ | tan ε = ε | cos γ | sec 2 ε cos 2 γ tan 2 ε + 1. (A2) In case (A), where 0 • < γ < 90 • , S true is given by equation (  17 ) and the uncertainty is derived by the following.
VLP signals About 40 minutes after the onset of the eruption, a swarm of VLP events began to be recorded at seismic stations near the terminal cone. Although they are not visible in the raw data due to short period energy of the eruptive tremor and ambient seismic noise, VLP waveforms become clearly visible after filtering the broadband records below 0. 1 Hz (see Fig. 4A ). Such low-frequency signals are clearly distinct from high-frequency VT earthquakes observed at Piton de la Fournaise as illustrated in Supplementary Fig. S2. VLP events are characterized by decaying harmonic lowfrequency oscillations with dominant periods near 20 s lasting for about 100 s. To investigate the corresponding particle motion, we  corrected the horizontal orientation of broadband sensors using teleseismic records of distant earthquakes (see details in the supplementary Text S3).
VLP signals depict radial polarization (Fig. 4B ), suggesting a source location approximately 1 km below the western edge of the Dolomieu crater. The VLP events were also recorded on tiltmeters located at the summit of the volcano. Fig. 5  shows a record from a Blum tiltmeter  (Blum, 1963)  that is co-located with the seismic station SNE (cf. , Fig. 1A ). As typically observed at Piton de la Fournaise, the tiltmeter data suggests a rapid inflation of the volcano summit at the onset of the magma injection followed by a deflation phase when the dike started to propagate toward the flank of the terminal cone  (Peltier et al. , 2005). We identify tilt offsets that are synchronous with VLP signals recorded on the broadband seismic stations. These tilt offsets are very small in magnitude (of the order of 10 -2 microradian) and are observed in a radial direction that is consistent with the VLP waveform polarization shown in Fig.
Acknowledgements We thank  Luis Rivera ,  Olivier Lengliné ,  Oryaelle Chevrel  and  Dimitri Zigone  for helpful discussion. We thank  Martin Vallée  for his help in estimating the orientation of broadband seismological stations. We are also thankful to  Diego Coppola  for providing TADR measurements. We thank  Simone Cesca  and two anonymous reviewers for their comments that allowed us to improve the manuscript. This project was supported by the  European Research Council  ( ERC , under the European Union's  Horizon 2020 research and innovation program  under grant agreement No. 805256 ). OVPF data is freely available through the VOLOBSIS (http://volobsis. ipgp. fr) and RESIF data portals (http://resif. fr).
This challenge requires us to identify a set of fundamental biological and ecological indicators that are (i) relevant across all marine ecosystems, (ii) have a strongly demonstrated link to OA, and (iii) have implications for ocean health and the provision of ecosystem services with impacts on local marine management strategies and economies. This paper draws on the understanding of biological impacts provided by the wealth of previous experiments, as well as the findings of recent meta-analyses, to propose five broad classes of biological indicators that, when coupled with environmental observations including carbonate chemistry, would allow the rate and severity of biological change in response to OA to be observed and compared. These broad indicators are applicable to different ecological systems, and the methods for data analysis suggested here would allow researchers to combine biological response data across regional and global scales by correlating rates of biological change with the rate of change in carbonate chemistry parameters.
, 2015). Substantial progress in this direction has been made  (Tilbrook et al. , 2019) , notwithstanding the fact that many geographical regions still lack sufficient capability, information, and data to determine local OA conditions. Best practices and standard operating mechanisms are now available to the global scientific community to assess spatial and temporal patterns in pH and carbonate chemistry, to quantify trends and rates of change, and to identify areas with strong signals of OA chemical impacts (IOC-UNESCO, 2019, and references therein). The second, even more challenging goal for the scientific community is to identify the impacts of OA on marine life, as they occur in real-world situations, over timescales that are appropriately matched to the rate at which the chemical changes are occurring and over spatial scales that are appro-priate for the biological processes being impacted.
To address this challenge, we need to identify a clear subset of biological variables and indicators that provide quantitative information across all ecosystems, as well as a method to not only understanding the rate and severity of the local impact, but also compare multiple datasets to deliver a greater holistic understanding of OA's biological impacts regionally and globally. Such methods will rely on biological observations being coupled with a suite of environmental observations including, but not limited to, measures of carbonate chemistry, in integrated observing activities. Here, we present an innovative approach that stems from considering five fundamental ecosystem traits that span across marine ecosystems and which have been identified as potentially sensitive to OA by a significant body of previously published studies.
However, our inclusive approach encourages the collection of carbonate chemistry and biological data that go beyond the defined set of observations that are recognized as either EOVs or EBVs. It should be noted that several biological EOVs remain to be fully defined, and their observation alongside physical and biogeochemical variables has yet to be implemented  (Miloslavich et al. , 2018). Our paper supports the ambitions outlined by  Muller-Karger et al. (2018b)  by proposing a practical way to detail the biotic impacts of ocean acidification by embracing a huge variety of biological observations. Such an approach would not only facilitate the creation of completely new observing activities that include both biological and biogeochemical observations from the start, but would also provide guidance on how existing biogeochemical ocean acidification monitoring can add relevant biological observations to their suite of observations, and vice versa, to better appreciate the biological consequences of OA across regional and global scales.
In both cases, molecular variants can be screened among individuals and populations in order to identify candidate genetic changes (de novo mutations and/or existing mutations) that might contribute to the ecological and evolutionary responses to particular environmental conditions such as OA  (Schaum et al. , 2018; Krasovec et al. , 2020). This association between molecular variants such as single-point mutations and the fitness of the organisms can be tested experimentally (e. g. ,  Schaum et al. , 2018; Waldvogel and Pfenninger, 2021) , linking geno-and phenotypes in evolving populations. This linkage could be achieved using traditional physiological experiments or by measuring the effects of the mutations on gene expression. Finally, mutations can be placed into a functional context by annotation of the protein sequences that host molecular variants using public databases such as the National Center for Biotechnology Information (NCBI) GenBank and the European Nucleotide Archive (ENA) (e.
By the inclusion of other potentially important environmental parameters, such as hypoxia, temperature, and pollution, it will also be possible to see how important OA is in driving biological response compared to the other environmental stressors for that particular biological trait. However, the overall purpose, and greater benefit, of increasing the volume of OA biological monitoring is to collectively use these data together in order to scale up site-specific observations and to thereby understand OA impacts within and across species and populations, geographical locations, and complex environments. For example, analyses could bring together multiple time series datasets to understanding which In scenario 1, the biological impact is largely caused by other environmental drivers, or the biological parameter that was measured is highly sensitive to OA.
Our innovative approach goes beyond the simple monitoring of specified biological traits. We propose a strategy that aims to compare the relationships between rates of chemical and biological change under different circumstances, allowing comparison across regional and global scales. We have shown that identifying robust rates of biological changes over a reasonable time (within a few decades) depends on local species, ecosystem sensitivities, and rates of chemical change and that the interpretation of observed biological responses needs to be mindful of this. We show that it is possible to use performance curves from experimental studies and data from chemical monitoring programs as a first-order step to identify the sites for biological monitoring that will give the most rapid indication of OA impacts, as well as the minimum duration to measure robust rates of biological change.
, 2020). In doing so, our approach to coupled biological and environmental observations will facilitate greater global connectivity between the collection of physical, biogeochemical, and biological essential ocean variables (EOVs) through the Global Ocean Observing System (GOOS) and essential biodiversity variables (EBVs) from the Group on Earth Observations Biodiversity Observation Network (GEO BON). Data availability. No datasets were used in this article. Author contributions. This article is a product of the work of the Biology Working Group of the Global Ocean Acidification Observing Network (GOA-ON, http://www. goa-on. org/, last access: 24 January 2023). All authors are members of this working group and significantly contributed to the development of the proposed framework, identification of indicators, and methodologies for the design of relevant biological monitoring strategies for ocean acidification.
, 2010) , Cyanothece-like organisms, (UCYN-C,  Foster et al. , 2007) , Richelia associated with Rhizosolenia (Het-1,  Church, Short, et al. , 2005) , Richelia associated with Hemiaulus (Het-2,  Foster et al. , 2007) , and Calothrix associated with Chaetoceros (Het-3,  Foster et al. , 2007). Descriptions of ddPCR reaction setup, droplet generation, thermocycling, thresholding, and detection limit calculations are provided in  Gradoville et al. (2021). For the temporal context, we also report nifH gene abundances of UCYN-A,  UCYN-B, Trichodesmium, Het-1, Het-2, and Het-3, measured at Station ALOHA between 2004 and 2017  (data set https://doi. org/10. 5281/ zenodo. 4477269). For these measurements, nifH gene abundances were determined using quantitative PCR (qPCR); details on sample collection, DNA extraction, and qPCR are provided in  Church et al.
Immediately following filtration, filters were placed in RNALater (Invitrogen, Waltham, MA, USA) and stored at -80°C until they were processed. Sample and data processing were performed using the methods described by  Gifford et al. (2016)  and  Wilson et al. (2017). Briefly, sample processing steps included RNA extraction, spiking with internal controls, cDNA synthesis, and sequencing using Illumina NextSeq 500 v2. Approximately two million 150-bp paired-end reads were produced for each sample; raw sequences are available on NCBI SRA under project number PRJNA596510. Reads were screened for quality, trimmed, and assembled after removing sequencing primers. Genes were mapped to the ALOHA 2. 0 gene catalog  (Luo et al. , 2020) , and annotated using the Genome Taxonomy Database (GTDB,  Parks et al.