dataesr/scientific-paragraphs-categorization huggingface dataset:
Pupillometry To analyze physiological correlates of uncertainty across conditions, we measured pupil dilation at a sampling rate of 1000 Hz in Experiments 1 and 2. We preprocessed data with resampling at 100 Hz and corrected for blinks in a window between -100 ms and +500 ms around the event of interest using a derivative padding window between -200 ms and +200 ms and an instantaneous derivative threshold of 15. We smoothed the data with a moving average window size of 50 ms, detrended slow fluctuations from the signal using a characteristic time constant of 30 s, and z-scored the data. We excluded from the analyses blocks in which participants had more than 50% of low-quality data due to blinks (N = 2 participants excluded from these analyses). We focused our analyses on phasic pupil RT-locked at the trial onset and response probe onset.
Acknowledgements MR is the beneficiary of a postdoctoral fellowship from the  AXA Research Fund. MR work was also supported by  La Fondation des Treilles  and department-wide grant from the  Agence Nationale de la Recherche  ( ANR-17-EURE-0017 , FrontCog). This work has received support under the program «Investissements d'Avenir» launched by the  French Government  and implemented by  ANR  ( ANR-10-IDEX-0001-02 PSL ). AW was supported by the  FIRE Doctoral School. VC was supported by the  French National Research Agency  ( ANR-16-CE37-0012-01  and  ANR-19-CE37-0014-01 ). JD was supported by the  James S McDonnell Foundation  (grant # 220020462 ). This work was supported by a starting grant from the  European Research Council  ( ERC-StG-759341 ) awarded to VW, a junior researcher grant from the  French National Research Agency  ( ANR-14-CE13-0028-01 ) awarded to VW, a  France-US.
collaborative research grant from the  French National Research Agency  ( ANR-17-NEUC-0001-02 ) and the  National Institute of Mental Health  ( 1R01MH115554-01 ) awarded to VW and JD. Foundation grant # 220020462   Jan Drugowitsch European Research Council ERC-StG-759341 Valentin Wyart Agence Nationale de la Recherche   ANR-14-CE13-0028-01   Valentin Wyart Agence Nationale de la Recherche   ANR-17-NEUC-0001-02   Jan Drugowitsch Valentin Wyart National Institute of Mental Health   1R01MH115554-01  Jan Drugowitsch Valentin Wyart The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Data availability Data and code availability. Participants' data and MATLAB code for behavioral and psychometric analyses of Experiments 1 and 2A, and code for fitting the computational model are available at https:// www. github. com/marionrouault/actobscom/, (copy archived at swh:1:rev:43bfb417394bd243aa4c-cf2ee2b2f50850d4cea8). Because tested participants did not provide written consent regarding the posting of their anonymized data on public repositories, the MEG, pupillometric and ECG datasets are available from the corresponding authors upon request. The following previously published dataset was used: Author(s) Year Dataset title Dataset URL Database and Identifier Wyart V, Weiss A, Chambon V, Lee JK, Drugowitsch J 2020 Weiss_2020_NatComm_ data_behavior https:// doi. org/ 10. 6084/ m9.
perenne grown under contrasted natural conditions. To this purpose, a sensitivity analysis was performed in order to determine the parameters with the highest effect on the HD. The most sensitive parameters were then estimated for seven cultivars on a subset of a data set provided by GEVES, the French Variety and Seed Study and Control Group. Finally, L-GrassF was evaluated for its ability to predict the HD on the second subset of data. The present analyses were performed on the main stems of plants only, as the available data did not allow us to assess the ability of the model to simulate the proportion of reproductive tillers.
The model was calibrated for L. perenne and is able to produce contrasted phenotypes from turf to forage types. The model is based on the L-system formalism, which allows a 3D and botanic representation of plant dynamics  (Prusinkiewicz 2004 ). It was implemented in L-Py, an L-system framework in Python distributed in the OpenAlea platform  (Boudon et al. 2012). L-GrassF was built upon the version of L-Grass implemented in the L-Py framework. The decomposition of the plant into individual tillers and organs was kept identical to that of L-Grass (Fig. 1 ). The main principles used to model the vegetative development in L-GrassF were derived from L-Grass, which however required several adaptions in order to extend L-GrassF to the reproductive stages. Briefly, we had to adapt two main aspects: (i) the time unit of the model was converted from thermal time to astronomic time and temperature-dependent processes were adapted by integrating a non-linear response function to temperature (Section 2.
4. 2; Fig. 1. 4). Floral induction dynamically affects tiller morphogenesis (Section 2. 1. 5; Fig. 1. 5, 6, 7) thereby determining the final morphology of the tiller (final number of leaves, number of long internodes and spikelets). Because the dynamics of internode and spike elongation are poorly documented for perennial grasses, we chose to approximate the HD as the date of ligulation of the flag leaf rather than from geometrical calculations (Fig. 1. 8). L-GrassF was calibrated on L. perenne but the underlying processes implemented are generic to grasses. The code of the model is open source, available on GitHub (https://github. com/ openalea-incubator/lgrass), and released under a CecILL-C license. The archive of the code used to generate the present results is available at https://doi.
org/10. 5281/zenodo. 6873725. ). A phenological model was developed, it calculates the rate of primary and secondary induction according to temperature and photoperiod  (3, 4). The progress of floral induction defines the rate of primordium initiation at the apex (5). Once the floral induction is completed, the floral transition is carried out and the final number of leaves is defined. The final number of spikelets is defined when the last leaf starts to elongate (6). Finally, after the elongation of all leaves is complete (7), the model simulates the heading of the spike from the ligulation date of the flag leaf of the tiller (8).
Leaf emergence and final length determination The leaf emerged when it exceeds the length of the pseudo-stem. The length of the pseudo-stem is calculated as the length of the longest sheath of the tiller. At leaf tip emergence, the final length of the leaf (Ym) is defined as well as the duration of its elongation (LED 0 )  (Verdenal et al. 2008). First, final leaf length (Ymn, mm) is assumed to increase with the time spent growing in the pseudo-stem until a maximum is reached, if this duration exceeds Age max (Equation (3)). Ym n = C × Ä T 01 e k1(min(Agen(tE n ), Agemax)+Tb1) - T 02 e k2(min(Agen(tE n ), Agemax)+Tb2) -L ä ( 3 ) where C is a scaling factor, Age n (t En ) (h) is leaf n age at emergence and T 01 (mm), T 02 (mm), k 1 (h -1 ), k 2 (h -1 ), Tb 1 (h), Tb 2 (h) and L (mm) are parameters fitted to data  (Verdenal et al.
Experimental data set The model was evaluated using a data set which describes the HD of several cultivars of L. perenne for several locations and years. The data set was obtained by GEVES, the French Variety and Seed Study and Control Group, which is responsible for the evaluation of candidate cultivars for their insertion in the French catalogue of cultivars. The assessment criteria of the cultivars include the HD, measured by GEVES as the first date when at least 10 spikes are visible per linear metre on row trials. The measurement consists of a visual assessment of the number of headed tillers every 3 days in six different sites in France. The HD of a cultivar corresponds to the average of three replicates. The sward management follows the National protocol  (GEVES 2006)  repeated in all locations and years.
Seeds are sown in spring in common gardens, in 6-m-long rows (5 g per linear metre) with three repetitions per cultivar. The rows are mowed during the first summer and the early winter. Plants are fertilized with N during their whole development to avoid nutritive stress. In the present study, we selected seven forage cultivars with contrasting HD and used as control varieties by GEVES (Bronsyn, Indiana, Bargala, Lactal, Milca, Carillon and Escal). The detailed characteristics of these cultivars and indicative values for the variables of interest are given in Table  2. The data set covers a period of 17 years  in six French locations (Table  3 ; see Supporting Information-Fig. S2 ). However, not all combinations of cultivar × location × year are available in the data set. Because the model does not account for drought effects, the data set was restricted to environmental conditions where water resources before heading were not limiting.
Environments where the water shortage would have potentially affected plant growth were identified using a water balance approach as follow. The soil relative water content (SRWC) was considered maximum (value of 1) on 1 December 2000 for all sites and updated daily considering rainfalls and evapotranspiration (after  Kunrath et al. 2015). In accordance with FAO  (Allen et al. 1994) , each day when SRWC was lower than 0. 4 was considered detrimental for the development of L. perenne. Finally, a site-year combination was removed from the data set when the period between the start of regrowth in spring and the HD (Table  2 ) included at least 25 % of days with SRWC < 0. 4.
So simulating plant development during that period would not have modified the floral status of the apices. Concerning the vegetative development, the 2 months between the sowing and the cutting date in our simulations allowed the tillers to reach a permanent From these databases, we extracted mean daily temperatures (which affect plant development and primary induction) as well as daily rainfall and potential evapotranspiration to exclude drought situations from the data set. The daily photoperiod was calculated by using the Python package 'ephem' (https://pypi. org/project/ephem/). L-GrassF was run in a high-performance computing centre (MESO@LR, Université de Montpellier, France).
Characteristics of the cultivars of Lolium perenne used for model evaluation. The trials were conducted by the GEVES institute and measurements were carried out on lines. Registration year is the year of the cultivar registration in the French catalogue. Start of regrowth in spring is the first date from 1 January when the height of stretched leaves exceeds 20 cm. Heading start is the first date from 1 January when at least 10 spikes are visible per linear metre. leaf production regime. Cutting the plants on 15 November as in real practice reduced the pseudo-stem length to 5 cm, leading to plant architecture close to that of the data set. Meteorological data used for the simulations were collected from the INRAE weather network (data available in the CLIMATIK database; https://intranet. inrae.
inrae. fr/clima-tik_v2) or from the 8-km gridded Météo France 'SAFRAN' database when the test site was too far from an INRAE weather station. Number of observations 35 36 32 26 27 26 23 Cultivar Breeder Country Registration year Ploidy Start of regrowth in spring (doy) Heading start (doy) Bronsyn Agriseeds Ltd NZ 2001 2n 68-98 124 Indiana Force Limagrain SA/DFL Trifolium FR/DK 2004 2n 82-98 135 Bargala Barenbrug Holland BV NL 2000 4n 82-92 135 Lactal RAGT/R 2n FR 2004 4n 92-105 142 Milca Carneau Frères SA FR 1996 2n 92-105 145 Carillon Carneau Frères SA FR 2002 2n 92-112 159 Escal R 2n FR 2005 4n -- DK: Denmark; FR, France; NL, Netherlands; NZ, New Zealand. Downloaded from https://academic. oup. com/insilicoplants/article/4/2/diac012/6632999 by guest on 14 September 2022.
While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets.
Models have been improving in the last 2 decades, but many models still have inaccurate results in the Arctic  (Shindell et al. , 2008; Eckhardt et al. , 2015; Emmons et al. , 2015; Sand et al. , 2017; Marelle et al. , 2018). That said, there has recently been a number of improvements in numerous models that have allowed for better representation of certain processes  (Morgenstern et al. , 2017; Emmons et al. , 2020a; Swart et al. , 2019; Holopainen et al. , 2020; Im et al. , 2021). In this study, model simulations for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report (AMAP, 2022) have been thoroughly evaluated by comparison to several freely available observational datasets in the Northern Hemisphere and assessed in more detail in the Arctic. In order to support the integrated assessment of climate and human health for AMAP, 6 SLCF species (methane -CH 4 , ozone -O 3 , black carbon -BC, sulfate -SO 2- 4 , organic aerosol -OA, and fine particulate matter -PM 2.
5 ) and 2 O 3 precursors (carbon monoxide -CO; nitrogen dioxide -NO 2 ) from 18 atmospheric or Earth system models are compared to numerous observational datasets (from three satellite instruments, seven monitoring networks, and nine measurement campaigns) for 4 years  (2008-2009 and 2014-2015) , with the goal of answering the following questions. 1. How well do the AMAP SLCF models perform in the context of measurements and their associated uncertainty? -What do the best-performing models have in common? -Are there regional patterns in the model biases? -Are there patterns in the model biases between SLCF species? 2. How does the model performance impact model applications, such as simulated climate and health impacts? 3. What processes should be improved or studied further for better model performance? Out of scope of this study are any sensitivity tests by the models to assess different components of model errors.
Models In this section we briefly describe the models used for the AMAP SLCF study and refer the reader to Appendix A for individual model descriptions and further information. All models were run globally with the same anthropogenic emissions dataset (see Sect. 2. 1), and most were run for the years 2008-2009 (as was done for the 2015 AMAP assessment report) and 2014-2015 (to evaluate more recent model results) inclusive for this evaluation, as these were years with numerous Arctic measurements. Unless otherwise indicated, all model output was monthly-averaged. The models used for this study are summarized in Table 1. As is shown in the table, not all models provided all SLCF species, and not all models provided all 4 years. There were eight chemical transport models (CTMs), two chemistry-climate models (CCMs), three global climate models (GCMs), and five Earth system models (ESMs).
Emissions All models used the same anthropogenic emissions dataset, which is called ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) v6B. These emissions were created using the IIASA-GAINS (International Institute for Applied Systems Analysis -Greenhouse gas -Air pollution Interactions and Synergies) model  (Amann et al. , 2011; Klimont et al. , 2017; Höglund-Isaksson et al. , 2020) , which provides emissions of long-lived greenhouse gases and shorter-lived species in a consistent framework. These historical emissions were provided for the years 1990 https://doi. org/10. 5194/acp-22-5775-2022 Atmos. Chem. Phys. , 22, 5775-5828, 2022 to 2015 at 5-year intervals, as well as the years  2008-2009 and 2014. Those models that simulated the 1990-2015 time period linearly interpolated the emissions for the years in between.
, 2020). CO and NO x emissions have been declining steadily and are expected to continue declining in the future. In comparison to the CMIP6 emissions  (Hoesly et al. , 2018) , ECLIPSEv6b emissions have additionally taken into account the recent declines in emissions from Asia of SO 2 , BC, and NO x due to recent control measures, whereas those declines in the CMIP6 emissions were unrealistically small  (Wang et al. , 2021; von Salzen et al. , 2022). The inclusion of emissions from the flaring sector in Russia was a significant improvement, which was not present in the previous version of ECLIPSE emissions that was used in the AMAP (2015a) report. For non-agricultural fire emissions, many models utilized the CMIP6 fire emissions, which are based on monthly GFED (Global Fire Emissions Database) v4.
1  (van Marle et al. , 2017). About half of the models included volcanic emissions or stratospheric aerosol concentrations from the CMIP6 dataset  (Thomason et al. , 2018)  or other sources, and the other half did not include volcanic emissions, which mainly impact SO 2 and thus modeled SO 2- 4. The emissions from the October to December 2014 Honoluraun volcano eruption  (Gíslason et al. , 2015; Twigg et al. , 2016; Ilyinskaya et al. , 2017)  were included by six models in a separate set of simulations. Similar differences in biogenic and agricultural waste emissions appear in these model simulations, and all are summarized in Table  2.
CH 4 and O 3 Global surface CH 4 measurements were obtained from the World Data Centre for Greenhouse Gases (WDCGG). These measurements were made via gas chromatography, which has a < 1 % uncertainty range. Surface in situ O 3 measurements are typically made via various types of UV absorption monitors, employing the Beer-Lambert law to relate UV absorption of O 3 at 254 nm directly to the concentration of O 3 in the sample air (e. g. ,  Bauguitte, 2014) , which have approximately a 3 % or 1-2 ppbv uncertainty range. We obtained surface O 3 measurements from various networks: the National Air Pollutant Surveillance Program (NAPS) and the Canadian Pollutant Monitoring Network (CAPMON) for Canada, the Chemical Speciation Network (CSN) for the US, the Beijing Air Quality and Hong Kong Environmental Protection Agency for China, the Climate Monitoring and Diagnostics Laboratory (CMDL) for some global sites, the European Monitoring and Evaluation Programme (EMEP), and some individual Arctic monitoring stations like Villum Research Station and Zeppelin Mountain.
Many of these measurements were downloaded from the EBAS database. The Arctic O 3 measurement locations are shown in Fig. 1. 3. 1. 2 CO, NO, and NO 2 CO and NO x measurements were obtained from the same monitoring networks as O 3. CO instrumentation is similar to that for O 3 ; however, it uses gas filter correlation to relate infrared absorption of CO at 4. 6 µm to the concentration of CO in the sample air  (Biraud, 2011). For NO x , the instrument deploys the characteristic chemiluminescence produced by the reaction between NO and O 3 , the intensity of which is pro-portional to the NO concentration. NO 2 measurements are approximated using its thermal reduction to NO by a heated (350 • C) molybdenum converter  (Bauguitte, 2014). Note that this method has an estimated bias of about 5 %-20 % because of sensitivity to other oxidized nitrogen species, and this has not been corrected for.
Satellite datasets Satellite observations are useful for evaluating models on larger horizontal spatial scales and for evaluating the threedimensional atmosphere -not the surface concentrations. Observations from three satellite instruments were used to evaluate model trace gas distributions in the free troposphere and, when appropriate, the lower stratosphere. These were the Tropospheric Emission Spectrometer version 7 (TES; NASA Atmospheric Science Data Centre, 2018;  Gluck, 2004a, b) , the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer version 4. 1 (ACE-FTS;  Bernath et al. , 2005; Sheese and Walker, 2020) , and the Measurements of Pollution in the Troposphere version 8 (MOPITT;  Ziskin, 2000; Deeter et al. , 2019). The vertical profiles of trace gas volume mixing ratios are derived or retrieved from the satellite-measured emission or absorption spectra, with varying degrees of vertical sensitivity.
Methane Measured annual mean surface methane is shown in the top left panel of Fig. 5 , along with model biases in the rest of the panels. Recall that unlike the rest of the SLCF species in this study, CH 4 concentrations were prescribed in these models from the same CH 4 dataset  (Olivié et al. , 2021). That said, the different decisions by modelers on how those CH 4 global concentrations are distributed make differences in how these models compare to measurements. The mean model biases are small and mainly positive; in the midlatitudes, the multimodel mean bias is +145 ppbv (or +8. 5 %), and in the Arctic, the mmm bias is 24 ppbv (or 1. 3 %), which means that the models simulate the magnitude of surface CH 4 well -though still outside the < 1 % measurement uncertainty range. There is a gradient in CH 4 VMRs (higher in the Northern Hemisphere and lower in the Southern Hemisphere) that is seen in the measurements (Fig.
While the ice cores contain BC data starting in the year 1750, only data after 1990 have been used to match the modeled time period  (1990-2015, 1995-2015, 2008-2009, and 2014-2015 , depending on the model). The measured BC deposition flux values on Greenland vary with elevation (lower fluxes at higher elevation). Summit (3177 m a. s. l. ) has an average of 285 µg m -2 yr -1 in contrast to ACT2 (2461 m a. s. l. ) with 676 µg m -2 yr -1. BC deposition is highest in the European Arctic at Spitsbergen with 856 µg m -2 yr -1. For all seven ice cores used in this comparison the averaged model mean is 3 times as high as the observations. At D4 (2728 m a. s. l. ) the modeled mean corre-   sponds best to the observation, with a mean bias of +83 %. At ACT11 (2296 m a. s. l. ) the models have 4 times the deposition flux compared to the measurements.
Since models were able to simulate this well, their improvement is likely due to improved Russian anthropogenic emissions in ECLIPSE v6B (Sect. 2. 1, AMAP, 2022) compared to previous emissions datasets, which did not include enough Russian flaring emissions. The best model results were from ECHAM-SALSA and MATCH when compared to all of the ship campaign data. Therefore, the general model evaluation for BC indicates that while there is a large variability in models results, they tend to overestimate surface BC at midlatitudes (including over the Pacific Ocean) and underestimate surface BC in the Arctic. Again, these results point to a lack of transport to the Arctic and, in this case, too much BC deposition along the way. While we were only able to evaluate BC deposition in the Arctic in this study, those results support the hypothesis of some models having too much BC deposition.