dataesr/scientific-paragraphs-categorization huggingface dataset:
METHOD DETAILS Fish community data Thirteen bottom-trawl surveys from 12 marine regions across the northern hemisphere were used to examine changes in the community temperature index (CTI) in fish communities over a large geographic scale with substantial longitudinal, latitudinal, and depth gradients. All surveys used similar sampling protocols, where bottom trawls were towed for an average of 30 minutes and the species composition and abundances of all captured fishes were identified and recorded (see Table  S1 ). Spatial coverage and resolution differed across surveys, and we therefore aggregated trawl surveys to 1° longitude × 1° latitude spatial grid cells. A 1° longitude × 1° latitude resolution was chosen to adequately capture both inter and intra-survey variation, to reveal gradients in community responses, to maximize data availability, and to match with the spatial resolution of the HadISST database (see 'Sea surface temperature' below).
We randomly selected four trawl surveys per grid cell, per year (four was the median number of trawls per cell, per year), recorded the resulting species' abundances, repeated this procedure 99 times, and calculated species' mean abundances across the 99 permutations. Only grid cells with with maximum sampling gaps of five years or less were considered (some surveys are only conducted every 3-5 years), resulting in a total of 558 cells. All survey abundance data were then log 10 (x+1) transformed before analyses. While we recognize that aggregating bottom trawl data to a 1° longitude × 1° latitude scale creates species assemblages that are not true locally interacting biological communities, we use the term 'community' for consistency with existing literature on concepts such as the community temperature index and community thermal diversity.
Sea surface temperature (SST) For each grid cell, we extracted mean-annual sea surface temperature (SST) and annual SST variation. Minimum and maximum SST were also initially considered, but later dropped because they were highly correlated with mean SST, but much less informative (i. e. , never had discernable effects in statistical models). SST data for each grid cell were derived from the Hadley Centre for Climate Prediction and Research's freely available HadISST1 database  46. The HadISST1 database provides global monthly SST on a 1° longitude × 1° latitude spatial grid and is available for all years since 1870. These data were used to examine temperature changes during the study period and to model the underlying processes of CTI.
Calculating community temperature index (CTI) Community temperature index (CTI) is the abundance-weighted mean thermal affinity of a community or assemblage, which reflects the relative abundance of warm-affinity or coldaffinity species  50. The inferred thermal affinity for each fish species in this study (1091 species total) was first calculated as the median temperature of each species' occurrences across its' global range of observations for which data were available (Figure  S2 ). Rather than surface temperature or bottom temperature, we used mid-water-column temperature (i. e. , from the surface to 200 meters depth) because the surveys included a mixture of demersal (bottom-living) and pelagic species. We used temperature climatologies from the global database WOD 2013 V2 (https://www.
nodc. noaa. gov/cgibin/OC5/woa13/woa13. pl?parameter=t) with a spatial resolution of ¼°. These climatologies represent average decadal temperatures for 1955-1964, 1965-1974, 1975-1984, 1985-1994,    1995-2004 and 2005-2012 on 40 depth layers. These data were aggregated vertically by calculating average temperature of the first 200 m depth. Species' occurrences were extracted from several databases including OBIS (https://obis. org/), GBIF (https://www. gbif. org/), VertNet (http://vertnet. org/) and ecoengine (https://ecoengine. berkeley. edu/). After removing duplicate occurrence records, we made a spatiotemporal match-up between temperature climatologies and species occurrences, considering both the geographic coordinates of occurrences, as well as their corresponding decade (to control for climate trends over the past 58 years).
We then took the median value of temperature from these records for each species. Although we included both demersal and pelagic species and used mid-water-column temperature to infer thermal affinities in our analyses, we tested the sensitivity of our results to these choices by recalculating thermal affinities using surface temperature and bottom temperature, both with and without pelagic species (see Supplementary Material). Separate data sources were used to calculate species' thermal affinities and to model the underlying processes of CTI because estimating species' thermal affinities required matching species' occurrences with mid-water column temperatures, whereas modelling the underlying processes required a standardized, continuous, temporally resolved database. Mid-watercolumn data were only available as decadal averages and did not cover the entire study period.
Initial SST was defined as the mean-annual SST for each grid cell for the period 1980-1989, the ten years prior to the study period. Depth was recorded during each trawl survey, and we calculated mean depth per grid cell. Distance to the nearest human population center came from Yeager at al. 47  , which is calculated as the straight-line distance to the nearest provincial capital as defined by the ESRI World Cities data set. Body size data came from the open-access trait database of Beukhof et al. 48. CTDIV was defined as the variation in thermal affinities in the community and was calculated as the abundance-weighted standard deviation of species' thermal affinities  9. CTR describes whether species in the community have narrow or wide thermal ranges and was calculated as the abundance-weighted mean of species' thermal ranges  9.
Body size again came from Beukhof et al. 48. We defined whether a species was commercially fished according to categories of commercial importance available from FishBase  49. Species listed as 'highly commercial', 'commercial', or 'minor commercial', were considered commercially fished, and species listed as 'of no interest', 'of potential interest', 'subsistence fisheries', or 'unknown' were considered not commercially fished. All models were performed using the R package lme4  52. Model quality and assumptions were verified using the R packages performance  53  and MuMin 54 (see Supplementary Material). Initial model inspection revealed low predictive accuracy and explained variation for the binomial models. This was likely because all species were initially included in this analysis whether they showed very slight or very large changes in abundance, i.
5 for all variables. For the model of differences between the strength of tropicalization and deborealization, the correlation between fitted values and observed values was 62% and the average cross validation accuracy was 57%. For the model of differences between the strength of borealization and deborealization, the correlation between fitted values and observed values was 79% and the average cross validation accuracy was 71%. For the four binomial models, we assessed binned residuals and predictive accuracy using the functions binned_residuals and performance_accuracy. Binned residuals are assessed by first ordering predicted probabilities from smallest to largest and calculating raw residuals. Data are then split into bins of equal numbers of observations and the average residual is plotted against the average predicted probability for each bin.
Acknowledgements We acknowledge the  MAESTRO group  funded by the synthesis center CESAB of the  French Foundation for Research on Biodiversity (FRB)  and  Filière France Pêche (FFP). M. M. and A. A. were supported by  Electricité de France  (RESTICLIM and  ECLIPSE  project),  IFREMER  ( ECLIPSE  project),  Région Hauts-de-France  and the  Foundation for Research on Biodiversity  ( ECLIPSE  project, contract no. astre 2014-10824). M. M. and M. A. M. were supported by the  Natural Sciences and Engineering Research Council  (Grant No. RGPBB/525590 ). M. A. M. was also supported by the  Canada Research Chairs Program  and the  Ocean Frontier Institute. M. L. and A. A. M. were supported by  VKR Centre for Ocean Life  and a  VILLUM research grant  (No. 13159 ). M. L. was also supported by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  862428  ( MISSION ATLANTIC ) and No.
However, these advantages come at a price: it is unrealistic to expect that on the timescale of the Voyage 2050 programme, large mm/sub-mm space-borne apertures comparable in size to Earth-based antennas can be deployed. Yet, by using receivers with system temperatures near the quantum limit and wide-band data acquisition systems, an acceptable baseline sensitivity can be achieved even with moderate in size space-borne antennas. This approach is pursued in several ongoing studies, including IRASSI (Infrared Astronomy Satellite Swarm Interferometry) for far-infra-red astronomy  (Linz et al. , 2020b)  and a concept of Event Horizon Imager (EHI), a step beyond the EHT requiring space-borne interferometric elements  (Martin-Neira et al. , 2017; Kudriashov et al. , 2018; Roelofs et al. , 2019).
The novelty of the SHAMISEN project was its holistic approach to health, where all aspects of the accident (technical, psychological, so cial, economic and ethical) were considered. In fact, experience suggested that the latter had not been sufficiently addressed by response plans in many European countries, and that existing recommendations had a mainly technical focus, with less attention paid to psychological, socioeconomic and ethical issues. Furthermore, there was a need to consider recent changes in legal and ethical requirements for health studies (e. g. those related to data protection). The current paper presents an overview of the SHAMISEN project, the results of which are described in more details in the other papers in this Special Issue  (Albani et al. , this issue; Barquinero et al.
Acknowledgements The  SHAMISEN  project was supported by the  EURATOM (European Atomic Energy Community ) programme of the  European Commission  in the framework of the  OPERRA  ( Open Project for the European Radiation Research Area ) project ( FP7  grant agreement No. 604984 ). ISGlobal acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Programme  (http://cerca. cat/en/). NMBU acknowledges the support of the  Research Council of Norway , Grant No. 263856.
PATIENTS AND METHODS Trial design. The design of the SENSCIS trial (ClinicalTrials. gov identifier: NCT02597933) has been published previously, together with the trial protocol  (11). Briefly, eligible subjects had SSc with onset of first non-Raynaud's phenomenon symptom <7 years before screening, extent of fibrotic ILD ≥10% on a highresolution computed tomography (HRCT) scan (based on assessment of the whole lung), FVC ≥40% predicted, and DLco of the lung (corrected for hemoglobin) 30-89% predicted. Subjects receiving prednisone ≤10 mg/day or equivalent and/or stable therapy with mycophenolate or methotrexate for ≥6 months prior to randomization were allowed to participate. Spirometers were provided to the sites and the results were confirmed centrally. Spirometry was performed in accordance with guidelines issued by the American Thoracic Society and European Respiratory Society  (14) , including daily calibration of the spirometer.
Several studies have shown that a decline in FVC is associated with mortality in patients with SSc-ILD. A study of 171 patients at a single center showed that patients who died within 4 years of SSc-ILD diagnosis had a higher annual rate of decline in FVC than those who died between 4 and 8 years after their SSc-ILD diagnosis or who survived for >8 years  (22). Data from Sclero derma Lung Study I (n = 158) showed that absolute declines in FVC of ≥10%  | 675 predicted or DLco of ≥15% predicted over 2 years were associated with mortality over a median follow-up period of 8 years, while data from Scleroderma Lung Study II (n = 142) showed that an absolute decline in FVC of ≥10% predicted at 1 year was associated with mortality over a median follow-up period of 4 years  (7). In a large cohort of subjects in the European Scleroderma Trials and Research (EUSTAR) database (n = 857), an absolute decline in FVC of ≥10% predicted, or an absolute decline in FVC of ≥5% predicted with a decline in DLco of ≥15% predicted, over 12 months was predictive of mortality over a maximum follow-up period of 5 years  (5).
, 2020)  and several individual modelling studies  (Chandan and Peltier, 2017, 2018; Hunter et al. , 2019; Chan and Abe-Ouchi, 2020; Feng et al. , 2020; Li et al. , 2020; Lurton et al. , 2020; Stepanek et al. , 2020; Tan et al. , 2020; Zhang et al. , 2020). In addition to these 15 models, MRI-CGCM  (Kamae et al. , 2016)    cause MRI-CGCM did not provide the AMOC results to the PlioMIP2 database, and HadGEM3-GC31-LL did not use the enhanced land-sea distribution condition with the Arctic gateways closed instead using the modern land-sea distribution. Note that five models come from the Community Climate System Model/Community Earth System Model (CCSM/CESM) family in the PlioMIP2 ensemble. To avoid these models taking undue weights in the PlioMIP2 ensemble, median instead of mean values are used in this study.
NorESM1-F ∼ 1. 0 • × 1. 0 • , k = 0. 10 cm 2 s -1 , Of the 15 PlioMIP2 models used here, 6 of them also took part in PlioMIP1. They are CCSM4, COSMOS, HadCM3, IPSL-CM5A-LR, MIROC4m and NorESM-L. However, all of these six models have submitted new pre-industrial control experiments to the PlioMIP2 database. CCSM4 has also been employed in a modified form by other modelling groups and is referred to herein as CCSM4-UoT and CCSM4-Utrecht. Therefore, the pre-industrial AMOC maximums and depths in PlioMIP2 are slightly different to the values in PlioMIP1.
Although the Arctic gateways may lead to a better agreement between simulated and reconstructed mid-Pliocene North Atlantic SSTs in some models, the effect is either not present for all of the models or it is not of sufficient amplitude to fully resolve the model-data discord. The PlioMIP2 models show a larger model spread in the simulated mid-Pliocene SST increases in the high-latitude North Atlantic, as well as the responses in the AMOC and North Atlantic OHT, relative to PlioMIP1. This reduced agreement is not surprising, as the model spread in global average surface temperatures is likewise more pronounced in PlioMIP2: 1. 86-3. 60 Compared with the PlioMIP1 ensemble in which the Arctic gateways were kept open, all PlioMIP2 models forced with the PRISM4 reconstructions that consider the closed Arctic gateways simulate an intensification in the mid-Pliocene AMOC.
org/10. 5194/cp-17-529-2021 mid-Pliocene simulations run with EC-Earth3-LR and the five models from the CCSM/CESM family (Fig. 6 ). For the other models, the range of warming at these sites is similar to that of PlioMIP1. This large model spread suggests that the reconstructed strong mid-Pliocene sea surface warming in the North Atlantic is not necessarily caused by the intensified AMOC and enhanced Atlantic northward OHT as suggested previously  (Dowsett et al. , 1992; Raymo et al. , 1996). Even given the intensified AMOC in PlioMIP2 due to the closed Arctic gateways, most models produce the mid-Pliocene North Atlantic sea surface warming that is weaker than the PRISM4 reconstruction  (Foley and Dowsett, 2019). Although the model-data discrepancy is reduced in the North Atlantic partly due to the intensified AMOC, the model-data mismatch remains large in other regions in PlioMIP2, for example sites 1081, 1082, 1084 and 1087 in the Benguela upwelling region (Fig.
, 2010; Small et al. , 2015). Taken together, these model-data discrepancies make it difficult to associate the intensified AMOC and enhanced Atlantic northward OHT with the reconstructed high mid-Pliocene SSTs. Fedorov et al. (2013)  suggested a possible mechanism for understanding the warm SSTs during the mPWP. Increased mixing in the subtropical ocean and reduced extra-tropical cloud albedo cause a strong warming in the mid-latitudes, including some upwelling regions. In PlioMIP2, CCSM4-UoT and CCSM4-Utrecht have considered increasing the ocean background mixing parameters, but no model has tested the impact of a reduction in the extratropical cloud albedo in the mid-Pliocene experiments. This mechanism can be further addressed in the future to investigate whether it is a suitable candidate for improving the simulation for upwelling regions.
In PlioMIP2, the model-data discrepancy is reduced in the North Atlantic, but the discrepancy remains large in the upwelling regions. The large model spread and the remaining model-data discrepancy suggests that an intensified AMOC and an enhanced Atlantic northward OHT cannot explain the reconstructed warm climate of the mid-Pliocene surface oceans. Data availability. To access the PlioMIP2 database, please send a request to Alan M. Haywood (a. m. haywood@leeds. ac. uk). PlioMIP2 data from CESM2, EC-Earth3-LR, GISS-E2-1-G, IPSL-CM6A-LR and NorESM1-F can be obtained from the Earth System Grid Federation (ESGF, 2020, https://esgf-node. llnl. gov/search/ cmip6/, last access: 3 December 2020). https://doi. org/10. 5194/cp-17-529-2021 Clim. Past, 17, 529-543, 2021.
The latter two parameters can be measured experimentally from the CSF velocity profiles processed with the GeneProfile interface. Our theoretical model describes a symmetric bidirectional flow for which the net total flow rate is null. In the user interface, before launching the fitting tool, we provide the user an estimate of the bidirectionality of the flow, called that is defined as: varies between 0% for a purely monodirectional flow and 100% for a purely bidirectional flow (the flow rate advected caudally equals the flow rate advected rostrally). We advise users not to perform the fit of velocity profiles for values of β < 70%, because under this arbitrary threshold, the "no flux" condition is no longer valid, and therefore the parameters of the fit are meaningless. 1. Once the flow profile is calculated (Data analysis Section A), the "Fit Model" button on the right (Figure  2A2 ) turns green.
The plot is also saved in. fig  4. Several information can be extracted from the fit, which are detailed below. The data are stored in either an. xls file, and/or a. mat file, called "TheoreticalProfiles", where: a. The velocity is stored in a 10000x1 vector called "Profile" (in μm/s) and the rostro-caudal position in a 10000x1 vector called "X" (in μm). b. The volume force generated by the cilia is stored in the variable called "f_v" (in N. m -3 ). c. The pressure gradient , opposing the cilia beating due to the closed geometry of the central canal, is stored in the variable "GradP" (in N. m -3 ). d. The width of the ciliary region, where the volume force is generated in the model, is stored in the variable "l_cilia" (in μm). e. The measured diameter of the central canal is stored in the variable "Diameter" (in μm).
The 5 maximal peaks of the Fourier spectrum are extracted, but, by default, only the first one is used. The frequency of the other peaks can be used for validation (e. g. , if sampling errors are made, the sum of the frequencies of the first and second peak is equal to the acquisition frequency). A 2D image with the main frequency found at each pixel is thus created (Figure  3C ). In noisy regions, it outputs a random frequency, but in cilia regions it draws regions of interests of a given frequency that we considered to be single cilia. Each of these regions of interest containing more than 40 pixels (7. 5  3. Run the code. 4. Select a folder where the. tif files with cilia imaging data can be found. If several acquisitions are located in the same folder, all. tif files will be analyzed sequentially, and cilia parameters from all experiments will be concatenated.
All MATLAB scripts and test files are shared on GitHub: https://github. com/wyartlab/BioProtocol_CSFflowMeasurement Osmolarity has to be 290 mOsM +/-3 mOsm, and pH needs to be adjusted to 7. 8 with NaOH. 2. TexasRed solutionPrepare a 0. 2% solution (w/v) of TexasRed in aCSF 3. α-bungarotoxin solution Prepare 1 μl stock aliquots of α-bungarotoxin at 1 mM Stock solutions can be stored at -20 °C and diluted in aCSF to get working concentrations of 100 µM for brain ventricle injections, and 500 µM for muscle injections 4. Injection Mix 1 For a 10 μl final volume: Mix 5 μl of FluoSpheres (2% v/v concentration in aCSF, sonicated) of FluoSpheres (2% v/v concentration in aCSF, sonicated) 4 μl TexasRed (0. 2% v/v concentration in aCSF) 1 μl α-bungarotoxin (1 mM).
Acknowledgments This work was funded by  Human Frontier Science Program (HFSP)   Research Grant  (grant n°  RGP063-2018 ), and the  New York Stem Cell Foundation (NYSCF)   Robertson Investigator award  (grant n°  NYSCF-R-NI39 ) for  Claire Wyart  and an ICM postdoctoral fellowship kindly attributed to OT and the  Big Brain Theory (BBT) program  from  ICM  to support the salary of YCB. The research leading to these results has also received funding from the  program 'Investissements d'avenir'   ANR-10-IAIHU-06  ( Big Brain Theory ICM Program ),  ANR-11-INBS-0011  ( NeurATRIS: Translational Research Infrastructure for Biotherapies in Neurosciences ).
6. Note: 6 All MATLAB scripts and test files are shared on GitHub: https://github. com/wyartlab/BioProtocol_CSFflowMeasurement Osmolarity has to be 290 mOsM +/-3 mOsm, and pH needs to be adjusted to 7. 8 with NaOH. 2. TexasRed solutionPrepare a 0. 2% solution (w/v) of TexasRed in aCSF 3. α-bungarotoxin solution Prepare 1 μl stock aliquots of α-bungarotoxin at 1 mM Stock solutions can be stored at -20 °C and diluted in aCSF to get working concentrations of 100 µM for brain ventricle injections, and 500 µM for muscle injections 4. Injection Mix 1 For a 10 μl final volume: Mix 5 μl of FluoSpheres (2% v/v concentration in aCSF, sonicated) of FluoSpheres (2% v/v concentration in aCSF, sonicated) 4 μl TexasRed (0. 2% v/v concentration in aCSF) 1 μl α-bungarotoxin (1 mM).
, 2016). On that particular day, two lightweight balloon-borne hygrometers intercepted a hydrated stratospheric air parcel emanating from two distinct overshooting plumes. However, no ice particles were detected by the particle counter and backscatter sondes. It is also worth noting that at these altitudes, the relative humidity with respect to ice was reported to be about 40 %-50 %. The paper is organised as follows: Sect. 2 gives a concise description of the observed case, as well as the TRO-Pico campaign and the balloon-borne devices utilised for WV measurements. The BRAMS model and the setup of the three simulations are described in Sect. 3. The TRO-Pico observed dataset is used to validate the simulations in Sect. 4. The key findings are discussed in Sect. 5, which depicts the structure and composition of overshooting plumes.
Only the Pico-SDLA and FLASH-B WV-measuring devices, along with the light optical aerosol counter (LOAC) and COBALD particle measurement equipment, were flown on 13 March 2012. The balloons collected data with a vertical resolution of approximately 20 m. Readers interested in balloon-borne measurement technology may read  Vernier et al. (2018)  and  Pommereau et al. (2011) , as well as the references in those papers, which are based on large balloon campaigns, BATAL and HIBISCUS, respectively. Pico-SDLA is an infrared laser hygrometer emitting at 2. 61 µm in a 1 m long open optical cell  (Ghysels et al. , 2016). Its uncertainty is about 4 % in the TTL conditions. FLASH-B is a Lyman-α hygrometer measuring WV at nighttime only with an uncertainty of 5 % in the UTLS  (Khaykin et al. , 2009). LOAC is an optical particle counter based on the scattered light at 60 • by ambient aerosol or particles for different wavelength channels  (Renard et al.