dataesr/scientific-paragraphs-categorization huggingface dataset:
S. F. S. also thanks  FGSE funding of the University of Lausanne. This work is also supported by Grant no. 205321_179084  from the  Swiss National Science Foundation , awarded to A. C. D. L. L. is supported by the  Czech Science Foundation  (Project no. 20-23550Y ) and by institutional support RVO 67985831 of the  Institute of Geology of the Czech Academy of Sciences. Fieldwork of J. C. G. -M. was funded by the  Spanish Ministry of Science and Innovation , Grants nr. CGL2017-87631-P  and  PID2021-125585NB-100. J. O. -H. is supported by the  National Science Foundation  (CAREER award no. 2047192 ). D. A. T. H. thanks the  Leverhulme Trust  for support. This paper is a contribution to the  TelluS-Syster  project ' Vers de nouvelles découvertes de gisements à préservation exceptionnelle dans l'Ordovicien du Maroc ' (2017) and the  TelluS-INTERRVIE  projects ' Mécanismes de préservation exceptionnelle dans la Formation des Fezouata ' (2018), and ' Géochimie d'un Lagerstätte de l'Ordovicien inférieur du Maroc ' (2019) all funded by the  INSU, CNRS.
Additionally, we have critically reassessed the published conodont data from 16 other sections from South China, and we have used this new, standardized dataset to construct the most accurate, highly resolved, and laterally reproducible biozonation of the Smithian to early Spathian interval for South China. The resulting 11 Unitary Association Zones (UAZ) are intercalibrated with lithological and chemostratigraphical (δ 13 C carb ) markers, as well as with ammonoid zones, thus providing a firm basis for an evolutionary meaningful and laterally consistent definition of the SSB. Our UAZ 8, which is characterized by the occurrence of Icriospathodus ex gr. crassatus, Triassospathodus symmetricus and Novispathodus brevissimus, is marked by a new evolutionary radiation of both conodonts and ammonoids and is within a positive peak in the carbon isotope record.
g. Brosse et al. , 2016; Guex, 1991; Monnet & Bucher, 2002). For the SSB, only a few recent studies have been based on this method:  Wu et al. (2019b)  constructed a biozonation for South China that includes 26 sections;  Chen et al. (2019)  constructed a Tethyan biozonation using four SSB sections, two from Oman, one from Slovenia and the well-documented Jiarong section from South China. Yet, these two studies led to strikingly different sets of characteristic species and they diverged in their respective positioning of the SSB. Based on the study of new and rich conodont collections from five sections (of which four are newly described here) from the Nanpanjiang basin, South China, we perform here a thorough taxonomical revision and describe a series of new taxa. By critically reassessing the published conodont data from 16 other sections from South China, and by applying the UA method to our taxonomically updated dataset, we constructed a new conodont-based biozonation of the Smithian to Early Spathian interval for South China with no internal contradictions and which is consistent with the global carbon isotope signal and the ammonoid zonation.
Biochronology For the construction of the biochronological scheme, specimens left in open nomenclature were excluded from the dataset. In addition to the new data presented here, we have included the published data from 16 well-sampled and well-documented sections, in particular those for which key taxa had been illustrated. These sections include Jiarong  (Chen et al. , 2015 (Chen et al. , , 2019)) , Motianling  (Wu et al. , 2019a) , Mingtang  (Liang et al. , 2016) , Guandao  (Lehrmann et al. , 2015) , Bianyang  (Yan et al. , 2013) , Qingyan  (Ji et al. , 2011) , Sidazhai  (Liang, 2017) , Ganheqiao  (Liang, 2017)  for the Nanpanjiang basin, and Daxiakou  (Zhao et al. , 2013) , Ganxi  (Lyu et al. , 2019) , Yiwagou  (Li et al. , 2019)  Jurong, Qingshan and Longtan  (Liu et al. , 2019) , West Pingdingshan  (Liang et al.
Their UAZ 2 partly correlates with our UAZ 5 , their UAZ 3 with our UAZ 6 , their UAZ 4 with our UAZ 7 , and their UAZ 7 with our UAZ 9. The conodont dataset produced by  Widmann et al. (2020)  was exclusively based on Qiakong, Shanggang, Laren and Lilong, without taking other published sections into account. The addition of 17 other sections as done here did not lead to substantial changes in the initial zonation proposed by  Widmann et al. (2020). This UA-based conodont biozonation of the SSB in SouthChina underlines the overall quality and completeness of the conodont record in these four initial sections. The minor modifications introduced here are mostly due to the larger temporal and spatial scope of the present study. Note that the latest Smithian UAZ 7 (equivalent to UAZ 5 in  Widmann et al.
Conclusion We reported five new SSB sections from the Nanpanjiang basin (Qiakong, Shanggang, Laren, Lilong and Youping Cascade) with high-resolution conodont and stable carbon isotope sampling. Using the abundant conodont material, we described one new conodont genus and 21 new conodont species from these sections. After having standardized the conodont taxonomy on the basis of 19 SSB sections from the South China Block, we processed the updated dataset by means of the Unitary Associations Method. The resulting, new, robust, high-resolution biozonation comprises 11 UAZs whose good lateral reproducibility ensures accuracy for correlating all the sections included in the data set. The new biozonation is also intercalibrated with ammonoid biochronozones, C-isotope chemostratigraphy and lithological markers.
Acknowledgements Guodun Kuang ,  Thomas Brühwiler ,  Chao Zhang ,  Thomas Galfetti ,  Arnaud Brayard  and  Björn Baresel  are thanked for their support in the field and for insightful discussions. M. Golding and  M. Orchard  are thanked for discussions on Olenekian conodonts and sharing data on segminiplanate conodonts in their collections. SEM imaging was performed at the  Center for Microscopy and Image Analysis, University of Zurich.
10 artificial glaciers are simulated with a Stokes ice flow model to produce input (geometry, sliding param. ) and output data (ice flow):. making sure that the dataset is diverse; ▶ small/large glaciers, ▶ narrow/large glaciers, ▶ thin/thick ice, ▶ gentle/steep slopes,. Step 2: set-up and train a CNN 3 check the CNN has learnt well The CNN recovers the Stokes ice flow of Aletsch 4 with 90% fidelity 4 Of course, Aletsch was not seen at training! Step 4: Substitute the CNN in glacier evolution model Comparing fidelity & performance with a simple advance/retreat run Well-trained CNN Total CPU time: ∼ 90 sec Total GPU time: ∼ 10 sec (Computational times refer to CPU and GPU of a same laptop) ⇒ animation ⇒ Simulation of Aletsch Glacier from 1880 to 2000 Goal: Find ice thickness h s. t. the emulated ice flow fit u s.
e. output real data), inverting occurs to find the input minimizing | ANN(input) -output | highly-efficient, mechanically complete, 3D forward/ glacier evolution model 1 with inversion 2 capabilities that features: ▶ open-source python code, easy to run and customize: ⇒ https://github. com/jouvetg/igm ▶ already-trained ice flow CNNs, but you may train your own: ⇒ https://github. com/jouvetg/dle ▶ run on both CPU and GPU (I have opened positions to work/develop IGM) References: Jouvet and al. , DL speeds up ice flow modelling by several orders of magnitudes, JOG, 2021. 2 Jouvet G. , Inversion of a Stokes glacier ice flow model emulated by DL, JOG, 2022.
▶1 10 artificial glaciers are simulated with a Stokes ice flow model to produce input (geometry, sliding param. ) and output data (ice flow):. making sure that the dataset is diverse; ▶ small/large glaciers, ▶ narrow/large glaciers, ▶ thin/thick ice, ▶ gentle/steep slopes,. Step 2: set-up and train a CNN 3 check the CNN has learnt well The CNN recovers the Stokes ice flow of Aletsch 4 with 90% fidelity 4 Of course, Aletsch was not seen at training! Step 4: Substitute the CNN in glacier evolution model Comparing fidelity & performance with a simple advance/retreat run Well-trained CNN Total CPU time: ∼ 90 sec Total GPU time: ∼ 10 sec (Computational times refer to CPU and GPU of a same laptop) ⇒ animation ⇒ Simulation of Aletsch Glacier from 1880 to 2000 Goal: Find ice thickness h s. t. the emulated ice flow fit u s.
1 , Additional File 1). Overall, all models explained a limited proportion of the deviance, making the predictions based on a single trait quite unreliable. From the PCA analysis on the six traits, we chose to keep the rst 3 dimensions (or Principal Components), because all their eigenvalues were superior to 1, and summed up to a total of 76. 6% of the original dataset inertia. According to the square cosine, the two hard traits were well represented by the rst two dimensions, capturing together 89% of the variation among strains in oxygen consumption and 77% in population growth rate (See Supplementary Table  2 , Additional File 3). For both traits, dimension 3 did not provide additional representation. On these dimensions, the other traits were represented at: 78% for NGDR, 41% for speed, 62% for cell size and only 5% for cell shape.
Still, even with a method that does not assume linearity (i. e. GAM), we could not nd pairwise relationships strong enough alone to allow reliable predictions of one trait using another. That absence of "good enough" relationships (i. e. which meets the points from the introduction: (i) a form that allows prediction and (ii) low standard deviation) could also be caused by a lack of statistical power. It is unlikely in our case since the analyzes were performed with a large sample size (n = 40), each data point furthermore being the average of many real replicate measures, a dataset that can be de nes as of high-quality for all the traits. However, we can see another two possible causes The rst one is an absence of tight relationships between these functional traits in T. thermophila. In this paragraph, we will take the example of trade-offs as an illustration of phenomena that can bind traits to each other.
Trait measurement Morphology and movement traits were measured from pictures and videos recorded under a dark eld microscope by placing two 10 µL samples from each mother culture into individual chambers on counting slides (ref: 630-2048, VWR ). We recorded one picture for each chamber and one 20 seconds video of one of the two chambers, randomly chosen. For all identi ed cells from the two pictures, cell size (its area in µm²) and shape (i. e. aspect ratio: major/minor ratio of the two axes of a tted ellipse; minimum is 1 for round cells, > 1 for more elongated cells) were quanti ed  (Pennekamp & Schtickzelle, 2013). From the video, we estimated cell speed (in µm/s) and trajectory tortuosity (as net to gross displacement ratio, NGDR) using the BEMOVI R-package  (Pennekamp et al. , 2015). NGDR is computed as a ratio of the net displacement (i.
6. 1  (50). To measure population growth rate, we created 2 mL experimental cultures by diluting each mother culture (i. e. one replicate of a given strain) in its stationary phase by a factor of ve, to allow exponential growth; experimental cultures were placed in 24-multiwell plates (ref: 662102, Greiner BioOne). Cell density was estimated every two hours over ve days using optical density at 550 nm (Synergy H1 microplate reader with robotic plate feeder, Biotek). The growth rate µ of each experimental culture was estimated as the slope of the optical density increase over time in its linear phase using the gc t function (gro t R package  (51) ).
Then, we performed two complementary analyses on our results. In a rst analysis, we explored the existence and linearity of simple pairwise relationships (i. e. a line) between functional traits, comparing GAM (gam function in mgcv R package (  52 )), against a GLM (glm (50)). GAM are an extension of GLM adding nonparametric smooth functions to a model, which allow the model to be non-linear  (53). This allows GAM to capture common nonlinear patterns that a classic linear model would miss. Adding an extra bending point (or knot) in the GAM allows for a better t (as adding an extra parameter in a GLM) but comes with a penalty, meaning that if the bending does not signi cantly improve the t to the data (increasing the deviance explained), it will not be retained. The presence of a signi cant pairwise relationship in the model can be checked with its deviance explained, and the linearity with the value of the effective degree of freedom (e.
Funding: The equipment used in that study was supported by an equipment grant from  UCLouvain  (U. N035. 16 ). N. A. S was supported by a PhD grant from  UCLouvain  (FSR) and by a  FRIA PhD  grant. T. M. -J. was supported by a  Move-In-Louvain Marie Curie Action postdoctoral fellowship  and by an  ERC grant  (EvoComBac:  949208 ). V. T. was supported by a  PhD grant  from  UCLouvain  ( ARC 10-15/31 ) and by a  FRIA PhD grant. N. S. is Senior Research Associate of the  F. R. S. -FNRS  and acknowledges nancial support from  UCLouvain  ( ARC 18-23/095 :  DIVERCE ). This paper is contribution BRCXXX of the  Biodiversity Research Centre at UCLouvain.
Introduction Global Change Observation Mission-Climate (GCOM-C), named SHIKISAI, was launched on 23 December 2017 and has been delivering continuous global Earth observations since 1 January 2018. The GCOM-C satellite carries the Second-generation GLobal Imager (SGLI) and aims to generate a data record for long-term monitoring of environmental change in the global coastal and open ocean. SGLI has multiple channels with high signal-to-noise ratio to detect the small ocean color signal, 250 m spatial resolution to allow better observation of the coastal oceans, and a wide swath (1150 km) to observe globally every two to three days (see Table  1 ). After the launch, stability of SGLI gains was evaluated (1) by using an internal lamp, (2) by measuring solar light through on-board diffusers  (Okamura et al.
2021 ) and the ocean color AC algorithm  (Toratani et al. 2020; 2021). The R rs derived by applying the k v and using the same LUTs is validated against R rs observed by Aerosol Robotic Network-Ocean Color (AERONET-OC)  (Zibordi et al. 2006 (Zibordi et al. , 2009 (Zibordi et al. , 2021)). The matchup dataset includes a large range of ocean color, surface reflectance, and aerosol conditions. The estimation error and sensitivity of R rs and AOT on the k v are evaluated for the two sets of aerosol LUTs. The pre-fixed k v in the red and NIR channels are discussed by referring them to those obtained from the aerosol and surface reflectance observations of AERONET-OC and those of the Radiometric Calibration Network (RadCalNet)  (Bouvet et al. 2019).
BOUSSOLE data BOUSSOLE has been operated in the Western Mediterranean Sea, 60 km off Nice, (43. 37° N, 7. 90° E) since September 2003  (Antoine et al. 2006 (Antoine et al. , 2008a;; b). The R rs used in this study was derived from hyperspectral measurements weighted by SGLI spectral response functions. The averages of R rs in the BOUSSOLE blue channels (average R rs of 0. 0042 at 443 nm) are about two times smaller than those in the MOBY blue channels (average R rs of 0. 0087 at 443 nm). This difference is due to BOUSSOLE data encompassing oligotrophic and mesotrophic conditions while conditions at MOBY are always oligotrophic. Another reason is that, for a given chlorophyll concentration, Case I waters of the Mediterranean Sea exhibit a larger contribution of absorption by colored dissolved organic matter than other oceans  (Morel and Gentili 2009; Morel et al.
AERONET-OC data AERONET-OC  (Zibordi et al. 2006 (Zibordi et al. , 2009 (Zibordi et al. , 2021) )  provides above-water radiometric data gathered with a system initially developed for atmospheric measurements of direct sun-irradiance and sky-radiance measurements and completed with a sea-viewing capability to derive reflectance. The uncertainty is at the 4-5% level for the water-leaving radiance data in the blue-green spectral regions  (Zibordi et al. 2009 ). We used the data from 23 AERONET-OC sites which operated in 2018-2020 to evaluate the influence of the vicarious calibration coefficients on R rs (see Sect. 4. 2). The multi-band data were converted to ρ w in the SGLI visible-NIR channels through the in-water model described in Appendix.
RadCalNET data RadCalNet  (Bouvet et al. 2019 ) is an initiative of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observation Satellites (CEOS) and provides a continuously updated archive of surface and TOA reflectances over a network of sites (currently, University of Arizona's site at Railroad Playa, Nevada, USA, the ESA/CNES site in Gobabeb, Namibia, AIR's site at Baotou, China, the CNES site at La Crau, France, the new AIR sandy site at Baotou, China) (https:// www. radca lnet. org/). We used the Railroad Playa and Gobabeb sites which are relatively homogeneous at the scale of several kilometers. We calculated land surface reflectance of SGLI channels from the RadCalNet measurements (at a 10 nm spectral sampling interval, in the spectral range from 380 to 2500 nm and at 30 min intervals) of the nearest time by weighting the spectral response functions of the SGLI channels.
JMA objective analysis data Column-integrated ozone and water vapor, sea surface wind speed, and surface pressure data were provided by the Japan Meteorological Agency (JMA). The ozone data are produced by the Meteorological Research Institute global chemistryclimate model (MRI-CCM2)  (Deushi and Shibata 2011)  which includes both the physical processes such as transport, vertical diffusion and deposition, and chemical processes between trace gases  (Shibata et al. 2005). These ancillary data were interpolated to the observation times and locations of the match-up data.
Calibration coefficients from different data sources and aerosol models The k v values obtained using different reference data are summarized in ) is about 1%. The difference on the R rs estimates can be reduced if we use k v derived by the same LUT. On the other hand, the 1% difference can have significant impact on R rs (as shown in Fig. 7 ) if we do not use the LUT which is selected to derive the k v. The difference between k v obtained with the MOBY dataset and the BOUSSOLE dataset is larger when using LUT-A than when using LUT-B (the difference for VN03 was 2. 6% and 1. 6% by LUT-A and LUT-B, respectively). That seems to be due to the same reason as for the R rs error discussed next (Sect. 5. 2).
Imprint of the BFZ through-flow in the Irminger Sea To assess the evolution of ISOW as the overflow exits the BFZ through the two western basins, we use L-ADCP profiles at the West section in 2015 and two Deep-Arvor floats deployed at the Middle section in 2018, #6902881 and #6902882. The analysis is based on L-ADCP instead of geostrophic velocities because the distance between the stations at the West section is too large to resolve the ISOW flow in these narrow basins. The data suggests that the main exit pathway for ISOW follows the northern walls of the western basins and that incursion of dense water from the Irminger Sea follows the southern walls, leading to a mixing between ISOW from the Irminger Sea and ISOW from the BFZ. Indeed, the RREX15 ship-based data show that each of the western basin is composed of a westward flow along the northern walls (stations 16 and 18) and an eastward flow along the southern walls (stations 17 and 19) in the ISOW layer (Figure  5 ).
Discussion and Conclusion A combination of new and insightful data sets allows us to investigate the role of the BFZ as a new source of ISOW for the Irminger Current flowing northward from the CGFZ. We first document ISOW pathways within the BFZ and its hydrological evolution by analyzing data from hydrographic sections and three Deep-Arvor floats deployed at specific locations through the BFZ between 2015 and 2018. From east to west, the hydrographic sections were acquired at the Eastern Sill, within the rift valley and through basins west of the Western Sill. This combination of observational datasets reveals that the BFZ is mainly fed by the lighter part of the ISOW layer flowing west of 29-30°W near 58°N, as part of the East Reykjanes Ridge Current (ERRC) in the Iceland Basin. ISOW is then channelled in the BFZ by the narrow and deep bathymetry of its Eastern Sill with an averaged ISOW transport of 0.
Acknowledgements. This work was carried out within the framework of the NAOS and ARGO-2030 projects. The two projects received the support of the  French government  within the framework of the " Investissements d'avenir" program  managed by the  Agence Nationale de la Recherche (ANR)  under the references  ANR-10-EQPX-40  and  ANR-21-ESRE-0019. The authors thank all colleagues and ship crews involved in the RREX and OVIDE cruises during which the hydrography data were obtained. The  OVIDE  project was supported by  CNRS ,  Ifremer , the national program  LEFE (Les Enveloppes Fluides et l'Environnement ) and the  Spanish Ministry of Sciences and Innovation co -funded by the  BOCATS2  Project ( PID2019-104279GB-C21 ) and  ARIOS  project ( CTM2016-76146-C3-1-R ).
Data availability. The hydrographic data obtained during the four cruises are archived online at https://www. seanoe. org/data/00443/55445/ for RREX15 and RREX17, at https://digital. csic. es/handle/10261/154341 for BOCATS16 and at https://www. seanoe. org/data/00762/87394/ for OVIDE18. The Deep-Arvor data of the floats used in this study are available at the Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), https://doi. org/10. 17882/42182. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo. ucsd. edu, https://www. ocean-ops. org). The Argo Program is part of the Global Ocean Observing System.
GPS Displacement Azimuth Variation We find that the azimuths of the co-seismic displacements are mostly parallel to the average strike (350°). However, south of the bay, around Palu City, GPS co-seismic displacement vectors point consistently more to the west than elsewhere at comparable distances to the fault. These details in the displacement field are important for the interpretation of slip and fault geometry below the bay, where no direct observation of the surface displacement is possible. He et al. (2019)  estimate a co-seismic 3D displacement field based on azimuth and range offsets from ALOS-2, from one ascending and one descending orbit and optical data from Sentinel-2. While they observe a similar displacement field as we present, their noise levels are higher, as SAR offsets and optical data have a lower precision than our data set.
Irrespective of local effects, our data and modeling indicate that fault bends have played a major role in the tsunamigenesis of the 2018 Palu earthquake; fault bends may be equally important to consider for tsunami hazards in comparable strike-slip settings. Some specifications to the CReDiT contributions. Data curation concerns collecting GPS data and maintaining the GPS network. For the formal analysis, W. S. has processed the GPS data and Y. M. has performed the SAR-data processing; W. S. and M. N. combined bathymetric and topographic data. T. B. has led the investigation process and has combined the different co-seismic displacement datasets into the combined displacement field; L. S. has conducted the finite fault inversion; O. K. has performed the tsunami modeling; N. N. has analyzed the fault directions; S.