dataesr/scientific-paragraphs-categorization huggingface dataset:
4 Derivation but no validation or internal validation or in retrospective databases. Rules requiring con rmation before being applied in a clinical framework. Although the clinical approach in terrestrial domestic animals is commonly applied, physical examination is insu ciently considered in marine molluscs and, regarding the latter, the literature does not describe a similar approach to assess a disease. As an example, when mortalities of the Paci c oyster Crassostrea gigas occur involving OsHV-1 (Oyster Herpesvirus type 1), the current diagnostic of this disease requires sacri cing animals to sample tissues before performing laboratory tests  (Schikorski et al. 2011 ). The manual of diagnostic tests for aquatic animals (OIE 2019) describes gross signs and behavioural changes of molluscs during infection with OsHV-1 but does not give bibliographic references nor explains the experimental protocol adopted to estimate these changes in infected hosts.
Statistical tests, expression of development and internal validation of clinical scale Each individual underwent a daily physical examination and a rating was attributed to it using the clinical scale (Fig. 1 ). All the statistical tests were performed with the Systat SigmaPlot® 3. 2 software. The statistical approach was performed according to the following sequence: 1) On the basis of the development data of the second experiment, a Cox univariate regression was applied. It permitted assessing the pertinence of the model and estimating the Hazard Ratio (HR) aimed at predicting the duration of survival of the Paci c oysters exposed to OsHV-1 as a function of the variable "rating assigned to the clinical scale". 2) On the basis of the internal validation of the second experiment, the discrimination was studied by expressing the cstatistic and establishing an ROC curve on different days of the experiment  (Tripepi et al.
Here, through a case study, we explore the potential to use the combination of shore-based camera video and tide gauges as a validation and analysis tool for coastal altimetry. The results show that measurements of sea level fluctuations on annual scales is similar between all three datasets. The analysis of the sea level data from both the tide gauge and the video camera which are co-located reveals that the physical contribution due to wave set-up and wave set-down accounts for 2% of the total sea level variations at monthly scale. We observe the loss of quality of the altimetryderived significant wave height at ~10 km from the coast, confirming the associated loss of accuracy in the sea state bias correction. Finally, this study highlights the critical need to co-locate the various measuring tools and devices at the coast with the satellite ground tracks if we want to optimally exploit coastal altimetry up to the coastline.
Presently, most altimetry based sea level products are limited to about 5-10 km from the coast. The rapid improvements in altimetry processing algorithms and in altimetry technology (e. g. the new generation of altimeters with a SAR measurement mode) allow new experimental coastal altimetry datasets, with sea level estimates even closer to the coast (up to 1-2 km,  Birol et al. , 2021). However, as these measurements are collected closer to the coast where waves may have a cumulative effect on both the sea level itself and the accuracy of the altimeter measurements, it is increasingly important to understand the impact of shallow and intermediate waves on satellite altimetry sea level measurements. In parallel, new validation tools that could help to understand the main factors that determine the altimetry data accuracy are needed.
Altimetry data From all available altimetry tracks in the study area, only the ones crossing the coast were considered here (Table  1 ). The Envisat and SARAL/Altika tracks 566 and 493 were not used because of the long repeat orbit of these missions, about 35 days, resulting in low data samples. The Jason missions, in contrast, had repeat collects every ~10 days and thus both Jason and Jason interleaved (called Jason 1N) orbits were considered. Data located in the lagoon area along the track 152 for Jason 1, 2 and 3 (a section of ~90 km large) were discarded from the analysis because they are representatives of very local conditions with almost no waves. The tracks and period of time considered for each mission are summarized in Table  1. Note that Jason missions have an inter-track distance of 315 km at the equator.
Video system and data The shore-based Argus video system at the FRF has coordinates 36. 18269 • N and -75. 75139 • E. The high-resolution video cameras were mounted on the top of the 43 m tall tower. This system has been upand running in different configurations (updates were done as the technology evolved) ever since the first installation in 1986. The optical intensity time series data that were used for this study, were collected by six video cameras at 2 Hz-frequency. Several ground control points were used for the photogrammetric transformation from pixel coordinates to local ground coordinates. The optical intensity time series data were merged every hour over the period 2010 to 2014 and every 30 min over the period 2015 to 2020 to obtain pixel arrays  (Holman and Haller, 2013; Holman and Stanley, 2007) , that started to be stored in 2010.
Buoys data Three directional buoys are located near the Duck tide gauge at the FRF (see Fig. 1 ): one at 17 m-depth, 3 km from the coast (36. 19970 N, -75. 71412 E); another at 26 m-depth, 15 km from the coast (36. 25881 N, -75. 59221 E) and the last at 49 m-depth, 94 km from the coast (36. 61100 N, -74. 84133 E). All these buoys are located out of the waves set-down and set-up area. However, the collected SWH and wave period data can be used to estimate the wave contribution to sea level. The SWH dataset used in this study is from the 26 m buoy as it is the buoy with the longest time series (from May 2008) located close to the tide gauge and the altimetry tracks at the coast. The latter collects data every 30 min and shows the following mean wave characteristics: SWH 1. 18 m, peak period T p 8.
The DAC and the SLA contributions were assumed to be constant along the video cross-shore profile. In order to evaluate the influence of all the contributors to total sea level, the contribution of astronomical tides (derived from the Global tide FES2012 model) have been considered. The relative contribution of each sea level component P i was estimated from the ratio of the individual variance to the total variance (ζ 1. SLA + ζ 2. DAC + ζ 3. SWH 2. T p ). (right), as a function of distance to the coast. The depth evolution is also reported. In the video, the sea level standard deviation vary between and 13. 5 cm. In altimetry, Jason 1N shows a greater stability of standard deviations along its track compared to Jason 1, 2, 3. This is probably related to the respective location of the data sets: fully on the continental shelf for Jason 1N, whereas Jason 1, 2, 3 crosses the continental shelf break.
Jason 1, 2, 3, also crosses the Gulf Stream current which certainly affects sea level dynamics and drives down similarities. Fig. 2  (right) shows sea level standard deviation increases more than 12. 5 m close to shore in Jason 1, 2, 3 and Jason 1N (0-33 km for Jason 1, 2, 3 and 3-8 km for Jason 1N). The sharp increase observed in Jason 1N is probably induced by errors due to land contamination of the altimeter's echo waveforms, however the data quality appear good up to 8 km from the coast. For Jason 1, 2, 3, the change in standard deviation observed is smooth and likely induced by the difference in ocean dynamics between the continental shelf and the area further offshore. The standard deviation of sea level variations derived from all the datasets closest to the Fig. 2. Shore and near shore sea levels variations (SL) from altimetry (Jason 1, 2, 3 on track 152 and Jason 1N on track 243) and video as a function of distance to the coast and of time at monthly timescale (left panels).
In the following analyses, we consider only data at depth shallower than 150 m, and compare monthly time-series of sea level derived from the satellite altimeters along the considered tracks with monthly timeseries of sea level from the tide gauge and from the video-derived data (Fig. 4 ). The considered video time-series was extracted at 180 m from the coast, which was the most correlated (r = 0. 93) with the tide gauge time-series. Two main observations stand out. First, altimetry derived time-series along the tracks are all similarly correlated with tide gauge and video, except for Jason 1N when approaching the coast (within 50 km), where the correlation with the tide gauge increases significantly. In contrast, within 8 km of the coast, there is a significant decrease in the correlation between Jason 1N and the video that is coincident with the sharp increase in Jason 1N sea level amplitude reported above (Fig.
3 km from the coast. And the observed wave-induced sea level amplitudes are consistent with the literature  (Raubenheimer et al. , 2001; Dean and Walton 2009). We also estimate the relative contributions of the DAC (i. e. sea level variations without the SLA, the wave's contribution and the tides) and the SLA (i. e. sea level variations without the DAC, the wave's contribution and the tides) for comparison. If this time we take into account the tides, the wave contribution represents 2% of the total sea level variations at the coast (Fig. 5B ). This spatial variability in the wave-driven contributions to sea level cannot be captured by the tide gauge and altimetry data. The tide gauge measures the sea level at one point whereas the altimetry data are still too far from the coast to be able to observe this wave process.
Conclusions This paper investigated how shore-based video systems, tide gauge and altimetry at the coastal area of Duck, NC, USA, respectively measure sea level variations. We also investigate how waves sign on sea level measurements at this coastal site. The results show that all data sets have the same annual signal with discrepancies which could be due to the waves. The wave-induced sea level variations at the coast, that is wave set-up and wave set-down, accounting for 2% of the total sea level variations, was revealed by analysing the sea level data from both the tide gauge and the video camera which are co-located. This study illustrates how co-locate tide gauges and video cameras with altimetry ground tracks might improve a multi-scale understanding of the processes affecting sea level at the coast.
Acknowledgements The authors are grateful to the  Center for Topographic studies of the Ocean and Hydrosphere (CTOH) at LEGOS (Toulouse, France)  for providing the altimetry 20-Hz X-TRACK dataset. Thanks are due to the  Coastal Data Information Program (CDIP)  for providing free access free access to the buoy data (https://cdip. ucsd. edu/m/products/). We are grateful to the  National Oceanic and Atmospheric Administration (NOAA)  for providing free access to tide data (https://tidesandcurrents. noaa. gov/). We are also grateful to AVISO + for providing dynamic atmospheric correction data (https://www. aviso. altimetry. fr/en/data /products/auxiliary-products/dynamic-atmospheric-correction. html).
The elements Ti, Mn and Ba were also detected, but are of low abundance in the standard and samples and were not used. A full dataset is in Online Resource 1. The method for determining in situ 87 Sr/ 86 Sr in plagioclase was the same as that described in  Shane et al. (2019)  and  Cocker et al. (2021). Briefly, the 87 Sr/ 86 Sr values were measured at the Centre for Trace Element Analysis, University of Otago using a Nu Plasma-HR multi-collector inductively coupled plasma mass spectrometer (MC-ICPMS), coupled to a RESOlution excimer laser ablation system. Typical 2σ uncertainties were ± 0. 00003 to 0. 00007 and represent instrumental and external uncertainties calculated from the reproducibility of repeat measurements of in-house standards and reference materials. BSE images of the phenocrysts were used to select sites for laser ablation (~100 μm wide and 400-600 μm long).
Higher magmatic water contents and temperature, and/or calcic melts will nucleate more calcic plagioclase. D'Mello et al. (  2023 ) estimated water contents for Taranaki magmas of ~1-3 wt % based on plagioclase-melt equilibria. This equates to K Ca/Na = ~1 based on several experimental andesitic/basaltic datasets (see  Martel et al. 2006). A caveat is that conditions may have changed during magma evolution. The presence of amphibole  (Stewart et al. 1996)  which is typically stable in andesites at higher water contents (e. g. , ~5-6 wt %, Martel Fig. 3  BSE images and compositions of phenocrysts selected to show typical relationships between texture and chemistry. a Type 1 phenocryst showing two major resorption surfaces not associated with change in Fe or Mg contents (r1, r2), and the one that is (r3).
We also investigated the Fe contents in the phenocrysts. Electron probe data expresses all Fe as FeO* and Fe 2 O 3 /FeO is unknown. However, the total Fe in plagioclase depends on the melt Fe 3+ /Fe 2+  (Wilke and Behrens 1999) , because the partition coefficient (K) for Fe 2 O 3 is significantly higher (~20 times) than that of FeO  (Lundgaard and Tegner 2004). Thus, an increase in melt Fe 2 O 3 /FeO could result in Fe-enriched plagioclase if other factors were constant. In contrast, K Fe2O3 and K FeO are not strongly correlated to plagioclase composition (An) but increase with the silica content of the melt  (Lundgaard and Tegner 2004). In natural magmas where Fe is an incompatible element in plagioclase, fractional crystallisation of Fe-Ti oxides will deplete the Fe in the melt producing Fe-poor plagioclase in more evolved melts.
Acknowledgements  David Neave  and an anonymous reviewer are thanked for their comments on the manuscript. Martin Mangler  and  Olivier Reubi  provided valuable insight on an earlier version. We thank  David Adams  for assistance with electron microprobe operation. Funding Open Access funding enabled and organized by  CAUL  and its Member Institutions This study was supported by the  Ministry for Business, Innovation and Employment Endeavour Fund  Contract  UOAX1913. PS also acknowledges the  University of Auckland Faculty Development Research Fund.
Earthquake hazard analyses rely on the availability of seismogenic source models. These are designed in different fashions, such as point sources or area sources, but the most effective is the three-dimensional representation of geological faults. We here refer to such models as fault sources. This study presents the European Fault-Source Model 2020 (EFSM20), which formed the basis for one of the primary input datasets of the recently released European Seismic Hazard Model 2020. The EFSM20 compilation was entirely based on reusable data from existing active fault regional compilations that were first blended and harmonized and then augmented by a set of derived parameters. These additional parameters were devised to enable users to formulate earthquake rate forecasts based on a seismic-moment balancing approach.
EFSM20 considers two main categories of seismogenic faults: crustal faults and subduction systems. The compiled dataset covers an area from the Mid-Atlantic Ridge to the Caucasus and from northern Africa to Iceland. It includes 1,248 crustal faults spanning a total length of ~95,100 km and four subduction systems, namely the Gibraltar, Calabrian, Hellenic, and Cyprus Arcs. The model focuses on an area encompassing a buffer of 300 km around all European countries (except for Overseas Countries and Territories, OTCs) and a maximum of 300 km depth for the subducting slabs. All the parameters required to develop a seismic source model for earthquake hazard analysis were determined for crustal faults and subduction systems. A statistical distribution of relevant seismotectonic parameters, such as faulting mechanisms, slip rates, moment rates, and prospective maximum magnitudes, is presented and discussed to address unsettled points in view of future updates and improvements.
The dataset, identified by the DOI https://doi. org/10. 13127/efsm20, is distributed as machine-readable files using open standards (Open Geospatial Consortium). This work documents the European Fault-Source Model 2020, EFSM20 (Basili et al. , 2022), a data product of the EU H2020 Project SERA (WP25-JRA3), designed to fulfill the requirements related to active faulting of the 2020 update of the European Seismic Hazard Model, ESHM20 (Danciu et al. , 2021, 2022), following the probabilistic framework established for the 2013 European Seismic Hazard Model, ESHM13 (Woessner et al. , 2015). To this end, the model aimed to cover a target area for foreseen ground motion that encompasses a buffer of 300 km around all European countries except for Overseas Countries and Territories (OTCs). The 300 km value estimate comes from the distance at which the ground motion propagated from a given source becomes negligible at the target site, based on ESHM13 ground motion model selection and outcomes (Delavaud et al.
This category includes the detachment at the base of the accretionary wedge, the interface between the two plates at crustal depth, and the dipping slab at mantle depth. These three elements are all part of the lower plate. The possible splay faults, branching upward into the upper plate from the slab interface, are included in the crustal faults category. The EFSM20 compilation is entirely based on published reusable data. It started from the Pan-European compilation European Database of Seismogenic Faults 2013, EDSF13  (Basili et al. , 2013)  and continued by exploiting several regional models that were updated or entirely designed afterward. Details about these key datasets and how they were used are given in Sections 2 and 3. We then performed data curation and harmonization to provide the user with all the necessary elements to develop a seismic source model and, ultimately, build an earthquake rate forecast.
Although EFSM20 was designed for developing earthquake hazard models, it can also assist post-earthquake analyses and tectonic or geodynamic modeling. The EFSM20 dataset includes 1,248 crustal faults spanning a total length of ~95,100 kmwith an individual end-to-end length range of ~4-900 km -and four subduction systems, namely Gibraltar, Calabrian, Hellenic, and Cyprus Arcs (Figure  1 ). The dataset distribution includes several layers providing different map feature realizations linked to relevant parameters. Such data layers are made available through the European Databases of Seismogenic Faults portal with a dedicated webpage (https://seismofaults. eu/efsm20) that directs the users to Open Geospatial Consortium (OGC) web services (WFS and WMS), downloadable GIS files in various formats (GeoJSON, ESRI shapefile, MapInfo tables).
The resolution of geological analyses limits our ability to identify with sufficient reliability only faults that can generate the largest earthquakes. However, the largest earthquakes have the longest recurrence time, so geologic-fault information can effectively complement the recurrence statistics of earthquake catalogs where they lack more data. The combination of spatial and temporal scales makes the fault data progressively more important as the earthquake magnitude increases. Although the continent scale of the collection prevented us from exploring each fault in great detail, and several fault parameters are affected by significant approximations, EFSM20 covers the major plate boundaries around the European plates and their interiors. The machine-readable fault attributes allow users to develop earthquake rate forecasts straightforwardly, promoting future updates to address unsettled points and meta-analyses and curiosity-driven studies to enhance our understanding of the seismogenic processes.
Methods The EFSM20 compilation was entirely based on reusable data. We started the initial collection from the Pan-European compilation EDSF13  (Basili et al. , 2013)  and progressively replaced it with up-to-date regional datasets. We considered primarily the compilations that covered with a consistent approach significantly large regions, relying on the work of the authors of each compilation regarding the accuracy and recency of the information. We resorted to working on individual studies or original work only in case of undefined situations, e. g. , area of overlap between two regional datasets or cases where a significant update was available or where the fault information was not covered by the initial pan-European dataset but deemed necessary. Regardless of size and coverage, all considered datasets must comply with a series of requirements.
Each crustal fault must have been declared active under one of the many existing definitions by the dataset authors or contributors. On the one hand, reviewing the definition of active fault was beyond the scope of this work. On the other hand, we recall that the definition of active faults may even differ in different tectonic settings. Subduction systems, instead, are included in the compilation regardless of the activity definition. The minimum set of basic fault parameters required for constructing a seismogenic source model refers to geometry (location: latitude, longitude, depth; size: length, width; orientation: strike, dip) and behavior (rake and slip rate). These are indispensable elements for devising and applying a fault recurrence model to be expressed by a Frequency-Magnitude Distribution (FMD).
Not all fault compilations fully provide this characterization, and strategies were devised to fill in the missing information and harmonize it. The next two subsections will describe these procedures separately for crustal faults and subduction systems. We assigned a unique identifier (ID) to each retained record in the collated dataset to avoid possible ambiguities in identifying the faults. The ID is a 7-character string, in which the first two positions are occupied by the letters "CF" for crustal faults and "SS" for subduction systems, followed by the standard ISO 3166 2-letter code, which identifies the country where most of the fault is located, followed by an alphanumeric 3-letter code (e. g. , CFCH0B5 identifies the crustal fault 0B5 which is in Switzerland). To track the provenance of each record, we assigned an identifier to the original dataset and stored the original fault identifier in that dataset.
Crustal faults The location and geometry of the mapped feature must be available through a set of coordinate pairs in a recognizable geographic coordinate system. The depth extent of the fault plane must have also been provided or derivable. The strike or dip direction, or any alternative strategy to provide data complying with the right-hand rule (e. g. , ordered sequence of nodes forming the mapped feature), and the dip angle were also indispensable to completing the geometric reconstruction of the fault plane in three dimensions. Regarding the fault behavior, the required parameters were the rake angle (or at least the prevailing sense of movement) and the slip rate. The strategy outlined above also adopted a set of prioritization criteria. The highest priority for collating the different datasets was given to the pan-European dataset because it guaranteed maximum spatial coverage with minimum effort.