dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This research has been supported by the  Pessoa program for science cooperation between Portugal and France , the  Programme Nationale de Planétologie in France , the  BQR programs of Laboratoire Lagrange and Observatoire de la Côte d'Azur. P. M. acknowledges support from the  Portuguese Fundação Para a Ciência  e a  Tecnologia  project  P-TUGA Ref. PTDC/FIS-AST/29942/2017  through national funds and by  FEDER  through  COMPETE 2020 (Ref. POCI-01-0145 FEDER-007672 ). We made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Sample Collection The study area is situated in the northern Mackenzie River basin (Figure  1 ). Suspended sediments were collected from four locations, comprising the Arctic Red River, the Peel River at Fort McPherson, the Mackenzie River at Tsiigehtchic near the apex of the delta before the confluence with the Arctic Red River and the largest channel within the Mackenzie Delta (Middle Channel), during peak freshet/receding water stages in  July 2013 , June 2017 , 2018 , and 2019 (Hilton et al. , 2015; Schwab et al. , 2020). We also use previously published data from May/June 2011 and September 2010 from  Hilton et al. (2015). Several transects were recorded by crossing the river normal to the flow, measuring channel depth and water velocity using Acoustic Doppler Current Profiling (ADCP; RioGrande, RD Instruments, 600 kHz in 2017-2018; RiverRay, RD Instruments, 600 kHz in 2019) mounted to the side of a boat.
Sedimentological Analyses Prior to particle size and SA analyses, OC was removed from freeze-dried samples by thermal oxidation (450°C, 6 h). Grain size distributions were measured using a Malvern Mastersizer 2000 laser diffraction granulometer (Malvern Instruments Ltd) coupled to a Hydro 2000S ultrasonic dispersion unit. Analytical reproducibility better than 10% was estimated based on the relative standard deviation of the modal size of repeated measurements. Surface area analyses were performed on a NOVA 4000e Surface Area Analyzer with N as adsorbing gas. Degassed samples (350°C, 8 h; Quantachrome FLOVAC degasser) were analyzed using the Quantachrome NovaWin software generating a 5-point BET method  (Brunauer et al. , 1938).
n-Alkanes derived from fresh vascular plants typically display an odd-over-even predominance for long-chain (n-C 25 +) homologs  (Bray & Evans, 1961; G. Eglinton & Hamilton, 1967) , whereas even homologs are often associated with biological or thermal degradation of organic matter  (Zhou et al. , 2005). The carbon preference index (CPI) uses molecular ratios to infer the degradation status of organic sources in sediments. While high CPI values reflect the input of fresh terrestrial matter, thermally altered or extensively degraded OC approach a CPI of 1  (Bray & Evans, 1961; Collister et al. , 1994; Freeman & Colarusso, 2001). In order to ensure comparability with other Arctic datasets  (Bröder, Tesi, Andersson et al. , 2016; Bröder, Tesi, Salvadó et al. , 2016; Tesi et al. , 2016; Vonk et al.
g. , helical flow) adding to the complexity of sediment transport patterns  (Baronas et al. , 2020). Consequently, a single river depth profile consisting of evenly spaced depth samples may be insufficient to accurately resolve suspended sediment and associated geochemical fluxes. Figure  6  demonstrates that statistical means obtained from measurement of depth-integrated samples retrieved from the Mackenzie River at Tsiigehtchic by the Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments (PARTNERS) and the Arctic Great Rivers Observatory (Arctic-GRO) programs  (Holmes et al. , 2021; McClelland et al. , 2016)  appear not to capture sediment-laden plumes. Specifically, POC in turbid transects collected from 2017 to 2019 in this study often display much higher concentrations for a given water yield than samples from the ArcticGRO data set.
2 of 21 M. S. Schwab was funded by the  Swiss National Science Foundation  through grants  SNF200020_163162/1  (" CAPS-LOCK II ") and  SNF200020_184865/1  (" CAPS-LOCK III "). R. G. Hilton was supported by the  European Research Council (ERC) Starting  Grant  678779 ("ROC-CO 2  ), and  NSF-NERC  Grant ( NE/ R001952/1 ). Materials were collected under the Scientific Research Licences  15288  and  16106  issued by the  Aurora Research Institute. The authors thank  Edwin Amos ,  Mathieu Dellinger ,  Christina Larkin ,  Gabriela Santilli ,  Barbara Lesniak , and  Daniel Montluçon  for assistance with sample collection. All.
| Outcomes The vividness scores of both the KVIQ-10 and TMIQ (i. e. Likert scale 1-5) along with the pain intensity (Numerical scale 0-10) were recorded using OpenSesame (version 3. 2. 4) software (https://osdoc. cogsci. nl/),  29 and stored as a csv file. This procedure was chosen to both automate In addition, maximal mouth opening was measured once using a calliper as recommended for both control participants and patients with TMD; 30 this measure was subsequently used for DC/TMD classification of intra-articular temporomandibular disorders for patients. F I G U R E.
REG IS TR ATI ONThe study was registered on ClinicalTrial. gov with identification number NCT04102306. CO N FLI C T O F I NTE R E ST None declared. AUTH O R CO NTR I B UTI O N S All authors listed in the manuscript meet all the criteria stated by the International Committee of Medical Journal Editors Recommendations for the conduct, reporting, editing and the publication of Scholarly Work in Medical Journals (ICMJE Recommendations 2018).
7 | 7 REG IS TR ATI ONThe study was registered on ClinicalTrial. gov with identification number NCT04102306. CO N FLI C T O F I NTE R E ST None declared. AUTH O R CO NTR I B UTI O N S All authors listed in the manuscript meet all the criteria stated by the International Committee of Medical Journal Editors Recommendations for the conduct, reporting, editing and the publication of Scholarly Work in Medical Journals (ICMJE Recommendations 2018).
In the Different Models, Same Initial Conditions (DIMOSIC) project, forecasts from different global medium-range forecast models have been created based on the same initial conditions. The dataset consists of 10-day deterministic forecasts from seven models and includes 122 forecast dates spanning one calendar year. All forecasts are initialized from the same ECMWF operational analyses to minimize the differences due to initialization. The models are run at or near their respective operational resolutions to explore similarities and differences between operational global forecast models. The main aims of this study are 1) to evaluate the forecast skill and how it depends on model formulation, 2) to assess systematic differences and errors at short lead times, 3) to compare multimodel ensemble spread to model uncertainty schemes, and 4) to identify models that generate similar solutions.
G lobal medium-range numerical weather predictions (NWP) are generated daily by several modeling centers, each of which develops largely independent forecast systems (observation handling, data assimilation, and the forecast model; for acronyms of centers and models, see Table  1 ). However, a continuous exchange of knowledge via conferences, scientific publications, and staff movements results in NWP systems that share similar fundamental concepts, for example, in dynamical cores and physical parameterization schemes. From this the main question for the DIMOSIC (Different Models, Same Initial Conditions) project arises: are we approaching a point where all models yield forecasts that are statistically similar? Errors in NWP forecasts originate from uncertainty in the initial conditions and imperfections in the models.
Therefore, following a complementary method to DYAMOND, one can use a set of models at a relatively lower resolution to perform multiple medium-range simulations and analyze the characteristics of these medium-range predictions. This idea promotes the form of the DIMOSIC project. The main aims of DIMOSIC are the following: • Identify and understand relationships between model formulation and forecast skill. By starting different models from the same initial conditions and comparing forecast skill for different regions, the strengths and weaknesses of individual models can be identified. • Assess systematic differences and errors at short lead times. The DIMOSIC protocol eliminates short-range biases that originate from the use of different data assimilation systems. Any rapid development of distinct biases is therefore an indication of systematic differences between the formulations of the forecast models.
• Compare multimodel ensemble spread to model uncertainty schemes. This may highlight areas where current model uncertainty schemes do not fully represent error sources related to model formulation. • Identify models that generate similar solutions. This information will help forecasters and end users to appreciate the significance of differences between guidance from different models. It will also help developers to identify systematic errors associated with specific model components. The institutes (models, key reference) represented in this report are DWD (ICON, https:// code. mpimet. mpg. de/projects/iconpublic), Météo-France (Arpege;  Roehrig et al. 2020) , ECMWF (IFS; ECMWF 2020), the Met Office (UM;  Walters et al. 2019) , Environment and Climate Change Canada (CMC-GEM; https://github.
com/ECCC-ASTD-MRD/gem), GFDL (SHiELD;  Harris et al. 2020) , and JMA (JMA-GSM; JMA 2019). Because most of the contributing modeling centers have experience with initializing their models with ECMWF operational analyses or reanalyses (ERA-Interim or ERA5), this became the choice for the common initial conditions. For each model, the same set of 10-day forecasts are initialized every third day for a 1-yr period (6 June 2018-4 June 2019). The model grid spacings are the same or similar to what is used operationally at each institute. We begin this paper by summarizing the contributing models and the compilation of the dataset. This section also describes the ingestion of the ECMWF initial conditions in each model and any adjustments that were required. Results are then presented in the form of forecast verification scores and mean errors (biases), augmented by an assessment of multimodel ensemble spread and similarities between models.
Models and data Model descriptions. All models that participate in the DIMOSIC project are global models used for medium-range weather forecasting. Table  2  provides a brief overview of the configurations of each model in terms of grid spacing, dynamical core, and selected physical parameterizations. The table also provides key references for each model that contain additional details about specific components. For the horizontal grid spacing, Arpege has a horizontally varying grid spacing with refinement to 5 km over Europe. Although all models have grid spacings between 5 and 25 km, they are in different stages of implementing methods believed to be important for kilometer-scale forecasts. The UM, SHiELD, and ICON dynamical cores are nonhydrostatic, while others are hydrostatic. The SHiELD and ICON models have finite-volume dynamical cores.
In this project we opted for a fixed mask. The mask is determined for each level by the following steps: 1) Determine the minimum value of the geopotential height over all models, dates, and lead times. 2) Interpolate the model orography from each model to 0. 5° with a maximum interpolation method (the maximum value of the contributing grid points are used). 3) Determine the maximum orography among the contributing models and add 20% of the height to avoid near-surface effects. For the precipitation verification, the NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset is used  (Hong et al. 2004). It is a high-frequency (half-hourly), high-resolution (0. 1°) satellite observational dataset covering the global area between 60°S and 60°N. Pradhan et al.
(2022)  note that Asia (which we will highlight in the following sections) is the subject of the most IMERG evaluation studies on the continental and country scale. Given the tremendous uncertainty in global precipitation analyses, the results may be sensitive to the choice of validation dataset  (Gehne et al. 2016) , and further investigation using other high-resolution precipitation datasets [e. g. , Multi-Source Weighted-Ensemble Precipitation (MSWEP);  Beck et al. 2019 ] is warranted. Initial conditions. All models in the comparison are initialized from ECMWF operational analyses based on IFS model cycle 45r1  (ECMWF 2018). The analysis fields are produced from the (mainly) strong-constraint 4D-Var data assimilation  (Rabier et al. 2000)  with a 6-h assimilation window (±3 h around the initialization time), while the first-guess forecast is provided from an analysis based on a 12-h window.
To test the sensitivity to the initialization of humidity, an additional experiment was run with the humidity from the native CMC-GEM analysis (CMC-GEM-Q). This resulted in a shock in the other direction with too much precipitation in the beginning of the forecast. The plot also includes forecasts from the native (own) analysis for UM, JMA-GSM, and for SHiELD based on GFS initial conditions. For these datasets a spinup period is still present, but not as strong as starting from the ECMWF analyses. The smallest magnitude shock is found for.
Discussion and conclusions In this paper we have presented examples of results from the model intercomparison project DIMOSIC, where different global models have been run from the same initial conditions. We have presented results for the RMSE of the forecasts, model biases and standard deviation of a multimodel ensemble, and differences between pairs of models. The initial conditions came from the ECMWF operational data assimilation. The choice was based on most of the global modeling centers having some experience from initializing from ECMWF operational or reanalyses (e. g. , ERA5). However, some models experienced a large initialization shock in terms of average precipitation, something that is to be investigated further. For the quality of the forecasts, for T500 over the N. Hemisphere, all models initialized with ECMWF analysis produced forecasts with RMSE much more similar to each other than forecasts initialized from their own analyses.
The DIMOSIC dataset provides a convenient way to investigate particular biases with the effect of different mean states of the initial conditions removed. Except for the examples discussed in this article, the DIMOSIC dataset has for example been used at ECMWF to understand biases in 700-hPa temperature during boreal summer  (Magnusson et al. 2022). A large diversity between forecasts was found when we compared the RMSD between the models, much higher than between two versions of IFS. ICON and IFS came out as the most similar in terms of RMSD between the models. This is not a surprise as the ICON model shares parts of its physical parameterizations with IFS. At the same time, the pair of ICON and SHiELD showed relatively large RMSD. However, it is difficult to point out a single model component that has the strongest impact on the forecast differences.
Climate Dyn. ). To facilitate such a study, we plan to make use of the Eulerian detection of warm conveyor belts  (Quinting and Grams 2022). The DIMOSIC project has provided a benchmark dataset that has already helped the different NWP centers with model development. For example, it has been greatly beneficial during the development of the Navy Environmental Prediction System Utilizing a Nonhydrostatic Engine (NEPTUNE) global model at NRL to identify systematic biases and also contributed to the identification of errors in other models. The dataset has also been used at KMA to benchmark the newly developed Korean Integrated Model (KIM). In this article we have focused on the forecasts of field variables (temperature, wind, geopotential, and precipitation). Ongoing work in the project will evaluate tropical and extratropical cyclones in the different forecasts and the results will be presented separately.
Acknowledgments. We thank the three reviewers that helped us to improve this article. We would like to acknowledge  David Richardson ,  Florian Pappenberger , and  Richard Forbes  for their input to this article. A special thanks to  Anabel Bowen  for helping to prepare the graphics for this article. James Doyle  and  Alex Reinecke  acknowledge the support of the  Office of Naval Research   PE 0602435N. Duncan Ackerley  was supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS and Defra. Data availability statement. All model data used in this paper can be requested from the corresponding author.
Acknowledgments V. H. acknowledges  B. Martinez  for providing the modules converting the PIC CALDER simulation output to a format suitable for the Monte Carlo FLUKA code. G. Sary is also thanked for his technical assistance in the FLUKA simulations. This work was supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 787539 , project  GENESIS ), and by Grant  ANR-17-CE30-0026-Pinnacle  from.
These simulations did not provide output on the temporal profile of the neutron pulse. In all Monte Carlo simulations, the converter target was a 3-cm-radius, finite-length cylinder, made of beryllium or lead. The former material is that used in state-of-the-art laser experiments while the latter is representative of high-Z converters, susceptible to spallation-type neutron generation for proton energies above ∼ 200 MeV  31, 77. Agence Nationale de la Recherche. We acknowledge GENCI-TGCC for granting us access to the supercomputer IRENE under Grants No. A0100507594 and A0110512993.
Funding: This study was supported by the  French Army Health Service , the  ANSES (Ploufragan Laboratory) , the  OpenHealth Company , the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the program " Investissements d'avenir ", reference  ANR-10-IAHU-03 , and the  Region Sud and European funding FEDER PRIMI. Institutional Review Board Statement: Not applicable.
87  Sr/ 86 Sr and elemental composition) have been applied to fracture-filling calcite veins and host carbonates from the Bóixols-Sant Corneli anticline, which developed along the front of the Bóixols thrust sheet in the Southern Pyrenees. This robust dataset is used to determine: (i) the absolute timing of fracturing and mineralization from fluid flow; (ii) the age and duration of fold evolution; and (iii) the variations and implications of fluid behavior across the anticline, as has been described in the article "Spatio-temporal variation.
Value of the Data • We present geochronological and geochemical data from fracture-filling calcite veins and carbonate host rocks from the outstanding exposed Bóixols-Sant Corneli anticline, along the front of the Bóixols thrust sheet (Southern Pyrenees). This robust dataset has been used to constrain the sequence of deformation, the age and duration of fold evolution, and the fluid flow behavior across the anticline. • We include the raw data that have been analyzed and discussed in the related research article. We also include the whole elemental and REE composition of calcite veins and host carbonates that has not been published yet. • These data are useful for geoscientists working on carbonate geochemistry and geochronology applying novel techniques such as U-Pb dating and clumped isotope thermometry.
Data Description Geochronological and geochemical data of fracture-filling calcite cements and carbonate host rocks exposed in the Bóixols-Sant Corneli anticline (Southern Pyrenees) are presented here. Samples were collected in ten representative localities that cover the different fracture networks as well as the sedimentary successions involved in the formation of the anticline. The location and description of samples are shown in Table  1  , the geochronological results in Table  2  and Fig. 1  , and the geochemical dataset in  Tables 3 , 4  and Fig. 2. The complete elemental and isotopic composition of veins cements and host rocks is found in the Repository. The main features of fractures, and the detailed petrographic characteristics of vein cements and host rocks is found in  [1]  and elsewhere in  [2 , 3].
2. In order to summarize this robust dataset and to describe the geochemical data, the analyzed vein cements have been assembled in three calcite groups (Group 1 to Group 3) according to three observed geochemical trends: The first geochemical trend (Group 1 calcites) has been observed in all cements from fractures and faults present in the hinge of the anticline (Cal Mestre locality) and in the base of the syn-orogenic deposits in the footwall of the Bóixols thrust sheet (Sant Antoni locality). The geochemistry of these calcites reflects the composition of their host rocks, either the Lower Cretaceous marls of the Lluçà Formation or the Upper Cretaceous marls of the Vallcarga Formation, respectively. Thus, Group 1 calcites yield minimum and maximum δ 13 C values between + 1. 3 and + 2. 4 ‰ VPDB, respectively, and 87 Sr/ 86 Sr between 0.
Signal strength at the ICP-MS was tuned for maximum sensitivity but keeping the oxide formation (monitored as 248 ThO/ 232 Th) below 0. 2% and low fractionation of the Th/U ratio. Static ablation used a spot size of 193 μm and a fluency of about 2 J/cm 2 at 12 Hz. Data were obtained in fully automated mode overnight in two sequences of 598 analyzes each one. Each analysis comprised 18 s of background acquisition, 18 s of sample ablation, and 25 s of washout. During 36 s of data acquisition, the signal of 206 Pb, 207 Pb, 208 Pb, 232 Th, and 238 U was detected by peak jumping in pulse-counting and analogue mode with a total integration time of ∼ 0. 1 s, resulting in 360 mass scans. Each spot was pre-ablated with 8 laser pulses to remove surface contamination before analysis. Soda-lime glass NIST SRM-612 was used as primary reference material (spot size of 50 μm, 8 Hz) together with four carbonate reference materials, which were bracketed in between the analysis of samples.
Raw data were corrected offline with an in-house VBA spreadsheet program  [7]. Following background correction, outliers ( ±2 σ ) were rejected based on the time-resolved 207 Pb/ 206 Pb, 208 Pb/ 206 Pb, 206 Pb/ 238 U, and 232 Th/ 238 U ratios. Such ratios were corrected for mass biases and drift over time, using NIST SRM-612. An additional matrix related offset was applied on the 206 Pb/ 238 U ratios (sequence 1: 21. 5%, sequence 2: 19. 6%) that was determined using WC-1 carbonate reference material  [8]. The 206 Pb/ 238 U downhole-fractionation was estimated to be 3%, based on the common Pb corrected WC-1 analyzes, and was applied to all carbonate analyzes. Uncertainties for each isotopic ratio are the quadratic addition of the within run precision, counting statistic uncertainties, excess of scatter (calculated from NIST SRM-612) and the excess of variance (calculated from WC-1) after drift correction  [9].