dataesr/scientific-paragraphs-categorization huggingface dataset:
For all possible pairs in G t (both that sharing and not any hosts) ten topological and viral characteristics were calculated as training features (Table  S4 ). Categorical features were one-hot-encoded and numeric features were scaled. An XGBClassifier model with binary: logistic family was trained using the feature dataset to predict if virus pairs share hosts (1,0 encoded output). The cross-validation was also used to determine the optimum decision threshold for determining binary classification (Fig. S6 ) and a precision-recall curve was used to identify positive predictive value and sensitivity at the optimum threshold (Fig. S8 ). The multiclass model was implemented in the same way, creating an observed network (G c ) based on species-level sharing of hosts and randomly dropping viruses to generate a training network (G t ) to train the XGboost model.
This generated a temporary network for the novel virus (G temp ). If the novel virus was not able to generate any edges with known viruses, meaning the host in which they have been found was never found positive for any known virus, predictions were not performed. 2. Using G temp feature values were calculated for the novel virus (betweenness centrality, clustering, and degree). For all possible pairs of the novel virus with known viruses that are not yet connected with each other through an edge in G temp a feature dataset was generated (Jaccard coefficient (novel virus, known virus) , the difference in betweenness centrality of the novel virus and known virus, if the novel virus and known virus were in the same cluster, the difference in degree centrality (novel virus, known virus) , if the novel virus and known virus were from same virus family, the difference in PubMed hits (novel virus, known virus) , PubMed hits for the novel virus, PubMed hits for the known virus).
Studies and nucleotide sequences for novel viruses are expected to be published and shared on PubMed's Nucleotide database and in various peer-reviewed publications. Data associated with GenBank accession numbers and nucleotide sequences for novel viruses are presented in Supplementary Data 3 and Supplementary Data 4 respectively. At the time of development of the model, data for all viruses was not shared in a format that would reflect on PubMed's database, we decided to use the number of unique species the virus was detected in the last ten years of wildlife surveillance conducted by the USAID PREDICT project. These detections will be reflected in PubMed's Nucleotide database and search term eventually, hence we considered them as a proxy for search terms conducted for known viruses. Currently, evaluation of the effects of this substitution of PubMed hits with the number of detections for novel viruses is not possible with limited data on novel viruses but needs to be reevaluated as more studies are published on these novel viruses.
To further evaluate the association between PubMed hits through search term and Genbank hits, we ran a generalized linear regression model with PubMed hits as dependent variable and Genbank hits as intendent variable, accounting for virus families. PubMed Search log À Á ¼ β 0 intercept þ β 1 Virusfamily categorical þ β 2 Genbank hits ðlogÞ The results indicated that Genbank hits had statistically significant predictive value in predicting PubMed hits (β = 0. 72, p < 0. 005) even after accounting for various virus families. Multiple virus families showed statistically different estimates than the reference virus family (Adenoviridae) indicating a significantly different association than other virus families. Results of the generalized linear regression model are presented in Table  S8. 3. Using this dataset for the novel virus, a binary presence of a link between the novel virus and known viruses was predicted using the trained binary model.
Data availability Data reported in this paper are available at https://zenodo. org/record/5899054, https:// data. usaid. gov/d/tqea-hwmr and https://data. usaid. gov/d/x3ij-fnrb, https://data. usaid. gov/Global-Health-Security-in-Development-GHSD-/PREDICT-Emerging-Pandemic-Threats-Project/tqea-hwmr.
The virus-host association data source is presented in supplementary code and data files (https://zenodo. org/record/5899054). searchterm ¼ ðþvirus name þ ½Title=AbstractÞ AND host OR hosts OR reservoir OR reservoirs OR ð wild OR wildlife OR domestic OR animal OR animals OR mammal OR bird OR birds OR aves OR avian OR avians OR vertebrate OR vertebrates OR surveillance OR sylvatic Á.
Unlike with MODIS, the EPIC PAR product does not exhibit gaps at low and middle latitudes. Accuracy is satisfactory for long-term studies of aquatic photosynthesis, especially given the much larger uncertainties on the fraction of PAR absorbed by live algae and the quantum yield of carbon fixation. The EPIC daily mean PAR product is generated operationally on a Plate Carrée (equal-angle) grid with 18. 4 km resolution at the equator and on an 18. 4 km equal-area grid, i. e. , it is fully compatible with the NASA Greenbelt OBPG ocean-color products. Data are available since the beginning of the DSCOVR mission (i. e. , June 2015) from the NASA Langley ASDC website.
These bands do not saturate over clouds, and they sample sufficiently the PAR spectral range. In this range, the scattering properties of molecules and aerosols vary smoothly with wavelength, gaseous absorption is relatively weak, and A is quite constant spectrally. The algorithm works on a pixel-by-pixel basis, i. e. , a daily mean PAR estimate is obtained for each pixel of the satellite imagery. The various processing steps and procedures are detailed in the following. First the bidirectional reflectance of the cloud/surface layer, ρ, is determined from the instantaneous EPIC Level 1b reflectance data at source resolution. This is only accomplished for pixels that are not contaminated by sea ice or Sun glint. For sea ice masking, fractional ice coverage at 25 km resolution from the National Snow and Ice Data Center (NSIDC) is used.
UNCERTAINTY ASSIGNMENT Associating uncertainty to each < E PAR > estimate is necessary to use properly the information provided by the estimate. It allows one to verify that variability and trends are actual to merge different datasets optimally, and to constrain adequately model predictions  (Frouin et al. , 2018a; IOCCG, 2019). Comparisons with contemporaneous and collocated in situ measurements at a few sites, the current approach to quantify experimental performance may not be sufficient to describe uncertainty in the wide range of conditions expected to be encountered. Representing uncertainty on a pixel-by-pixel basis requires modeling the measurement, identifying all possible error sources (e. g. , noise in the input variables, imperfect or incomplete mathematical model), and determining the combined uncertainty  (JGCM-100, 2008; Povey and Grainger, 2015; IOCCG, 2019).
The bias and standard deviation portions are calculated as a function of clear sky daily mean PAR, < E clearPAR > , and cloud factor, < CF PAR > < E PAR > / < E clearPAR > (characterizes the effect of clouds on PAR and varies from 0 to 1), by simulating for many situations the satellite measurements and corresponding < E PAR > and comparing the latter to the < E PAR > estimated from the TOA reflectance. The simulations were performed with the Atmospheric Radiative Transfer Database for Earth and Climate Observation (ARTDECO) code  (Dubuisson et al. , 2016 ; https://www. icare. univ-lille. fr/artdeco/) using as input several years (2003 to 2012) of MERRA-2 hourly data (aerosol and cloud properties). The large number of data points allows one to sample a wide range of geometric configurations for the satellite data and atmospheric variability, in particular many situations of daytime nebulosity for all latitudes.
Datasets The EPIC <E PAR > product has been evaluated against in situ measurements at three mid-latitude oceanic sites (Figure  4 ), where long-term E measurements are routinely acquired from moored buoys i. e. , BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE) and California Current Ecosystem (CCE) buoys 1 and 2 (hereafter denoted CCE-1 and CCE-2). Table  2  lists the main characteristics of the data sets. Although situations for which Sun zenith angles stay large during the day (such as in polar regions) were not sampled, the atmospheric conditions at the three sites exhibited large variability in cloud and aerosol properties making the datasets appropriate for statistically quantifying uncertainties in the < E PAR > estimates. The BOUSSOLE above-surface downward solar irradiance dataset (http://www.
obs-vlfr. fr/Boussole/html/project/boussole. php;  Antoine et al. , 2008)  consists of high frequency E PAR measurements (every 15 min) collected from the long-term mooring located at 43. 37 °N and 7. 90 °E in the Western Mediterranean Sea about 60 km off the coast (between Nice and Corsica, France). The E PAR measurements were made by Satlantic cosine radiometers installed on top of the immerged part of the buoy during May 29, 2015-July 28, 2019 (i. e. , corresponding to the EPIC operational phase). Four deployments were made during this period, but there was a large data gap between December 11, 2017 and February 16, 2019. Before conversion into geophysical quantities, the raw data were calibrated using coefficients provided by the manufacturer and checked for outliers. The E PAR measurements were corrected for tilt effects according to  Antoine et al.
(2008). To obtain daily mean values, i. e. , < E PAR > , instantaneous E PAR with tilt angles less than 20 o was integrated over time from sunrise to sunset. The CCE-1 and -2 datasets were collected at two surface moorings in the California Current (http://mooring. ucsd. edu/ cce/). Multiple deployments are available and for this study we used the CCE-1 deployments from October 23, 2017 to June 9, 2020, and the CCE-2 deployments from August 15, 2017 to May 7, 2019. CCE-1 is located at 33. 46 °N, 122. 53 °W in the core of California Current, approximately 220 km off Point Conception, California. The CCE-2 mooring is operated at 34. 31 °N and 120. 80 °W and closer to the shore, approximately 35 km off Point Conception. For both mooring locations, the E measurements were made every 30 min at 412 nm, 443 nm, 490 nm, 510 nm, 555 nm, 620 nm, and 669 nm by Sea-Bird OCR-507 sensors.
The raw data were calibrated to actual E using radiative transfer (RT) simulations. First, clear sky days were identified by carefully examining the shape of raw data as a function of time each day. Only those following strictly the cosine function E PAR acos(θ s )e -β/cos(θs) with limited error  (Tan et al. , 2020, Section 4. 1) were selected. Second, the theoretical E for these clear days were simulated using the 6S code with aerosol properties, water vapor, and ozone, wind speed, and chlorophyll-a concentration from NASA OBPG MODerate resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) Level 2 products. Only days with τ aer less than 0. 1 at 550 nm were retained and θ s was limited to 60 o. For those τ aer and θ s conditions the aerosol transmittance, about 1 -0.
Calibration and Adjustment The calibrated datasets need to be checked and eventual biases removed before evaluating the EPIC < E PAR > estimates. This is important because laboratory calibration errors and other errors (e. g. , due to exposure to the marine environment and data processing) may significantly affect the quality of the in situ measurement. An independent check-of-calibration can be performed, as indicated above, by comparing the calibrated measurements to RT simulations in clear sky conditions that allow accurate computations of the atmospheric transmittance. Since the CCE-1 and -2 datasets were already calibrated using such RT simulations, as described in the previous sub-section, no further bias adjustment is necessary for those datasets. The BOUSSOLE dataset, however, was checked against 6S simulations.
The same procedure as described for CCE-1 and -2 datasets, including the selection of clear sky days with small aerosol content and θ s less than 60 o , was used. Only five suitable clear sky days were identified during deployments #3 (June 29, 2016-May 24, 2017) and #5 (February 16, 2019-July 28, 2019); the processing, therefore, was limited to those deployments. Corrections were then applied to the in situ data based on the best linear fits (y ax) obtained by regressing, for each deployment separately, instantaneous E simulations against corresponding in situ measurements. Figure  6  displays scatter plots of 6S-simulated versus measured E for the two BOUSSOLE deployments before and after correction. The overall bias before correction (Figure  6 , left) decreased from 0. 55 Em -2 d -1 (0.
4%) to -0. 14 Em -2 d -1 (-0. 1%) after correction (Figure  6 , right) and RMSD from 2. 07 Em -2 d -1 (1. 6%) to 1. 69 Em -2 d -1 (1. 3%). The small bias and RMSD in the BOUSSOLE data before correction indicate that the BOUSSOLE data are well-calibrated for those deployments and suggests that one may use with confidence the data from other deployments, even without adjustment. Examination of those deployments, however, revealed abnormal E values. Therefore, only data from deployments 3 and 5 were used, and for consistency with the CCE-1 and -2 datasets the small bias adjustment was still applied to the data.
Match-Up Comparison EPIC < E PAR > estimates remapped at 18. 4 km spatial resolution (equal-area grid) were matched with in situ measurements at the three evaluation sites. The EPIC pixel with center closest to the site was selected. One may wonder whether, due to the size of the EPIC < E PAR > pixel, satellite estimates are mismatched with the local in situ measurements. Comparisons of MODIS-Aqua and -Terra < E PAR > estimates at 4. 6, 9. 2, and 18. 4 km resolution during June 13, 2015 (beginning of the EPIC dataset), to June 23, 2021, indicated practically no biases at the BOUSSOLE and CCE-1 sites and a slight overestimation by about 0. 5-0. 7% at 18. 4 km resolution with respect to 9. 2 and 4. 6 km resolution, respectively, at the CCE-2 site. RMSD was 3. 9-4. 7% between estimates at 9. 2 and 18.
Uncertainty may also be specified as a function of angular geometry and latitude, even region, instead of using an average estimate for all latitudes over several years of MERRA-2 data, and they can be fitted by a generalized additive model with proper auxiliary variables. Other < E PAR > datasets exist (e. g. , OBPG MODIS and VIIRS products), but the EPIC time series is uniquely valuable for several reasons. First, the daily mean PAR estimates, which exploit the multiple EPIC observations from sunrise to sunset (i. e. , consider diurnal cloud variability), are more accurate than those from sensors in polar orbit (i. e. , typically use one observation per day). Second, coverage is global on a daily time scale at low and middle latitudes, which is currently not achieved with instruments onboard polar orbiters (due to Sun glint, limited swath).
It complements existing < E PAR > datasets and, as mentioned above, may bring about consistency across sensors, allowing a better description of biological phenomena that could lead to new information about temporal variability of biological processes. The methodology can be easily extended to estimating ultraviolet (UV) surface irradiance using the EPIC spectral bands centered on 317, 325, 340, and 388 nm, especially since ozone content, a key variable governing atmospheric transmittance in the UV, is a standard EPIC product. Furthermore, planar and scalar fluxes below the surface, as well as average cosine for total light (a measure of the angular structure of the light field), variables more directly relevant to addressing science questions pertaining to biogeochemical cycling of carbon, nutrients, and oxygen can also be estimated without major difficulty from the above-surface quantities.
ACKNOWLEDGMENTS The authors gratefully acknowledge the various teams that produced and quality-controlled the EPIC data used in the study, the  NASA OBPG  for generating and making available the MODIS and VIIRS products, and  JAXA  for providing the AHI imagery via the P-Trees System. They also thank everyone involved in servicing the field instruments and collecting, processing, and archiving the BOUSSOLE, CCE-1 and CCE-2, and COVE solar irradiance data, and the  MERRA-2 Science Team  for the reanalysis data. The CCE-1 and CCE-2 moorings are part of the  global OceanSITES program.
FUNDING The work effort to accomplish the study was funded by the  National Aeronautics and Space Administration (NASA)  under DSCOVR Grant  80NSSC19K0764  ( Program Manager: R. S. Eckman ) and by the  Japan Aerospace Exploration Agency (JAXA)  under GCOM-C Contract  JX-PSPC-515384. The CCE moorings are funded by  National Oceanic and Atmospheric Administration (NOAA) GOMO  and  OAP  under Grant  OAR4320278  Award#  304593.
The degradations techniques are detailed in Section S1, Supporting Information. The limitation with those large scale, full and half cells approaches is that it is difficult to obtain local data at the polymer scale. The second concern is that without extensive deviation from the nominal conditions of use, the degradation process will take from several weeks to several months. Scanning SECM provides access to the local electrochemical properties of the electrode under study. The local area affected directly depends on the size of the UltraMicroElectrode (UME) tip used. SECM has already been used to monitor Li-ion battery electrode degradation through in situ characterization of the solid electrolyte interface (SEI). [19] Cyclic voltammetry studies were made to characterize lithium (de)insertion on single LiMnO 4 particles using SECM.
The second advantage of this technique concerns the degradation conditions which are identical to the operational conditions found in a complete device. Finally, the analysis and the degradation are done in situ, using a single instrument. To our knowledge, this is the first ever study to use SECM as a mean of local and fast degradation for ageing purposes. SECM was first used in feedback mode to evaluate the kinetic parameters of the surface reaction of the redox mediator and the nanocomposite polymer electrode. This approximation was used to feed a finite element model of the SECM experiment using COMSOL Multiphysic software suite. The electrodes were then degraded using SECM in chronoamperometric mode to locally degrade the electrode in an accelerated way. Degradation characteristics of the electrodes were evaluated using the finite element model to infer the conducting polymer degradation mechanism occurring on the different electrodes.
The first and second columns list the groups and models used. The third and fourth columns indicate the atmosphere and ocean nominal resolution. The last row at the bottom of the table lists the variables that were used for our study: sea surface temperature (SST; called tos in the HighResMIP database), zonal and meridional wind stress components (tauuo and tauvo), sea surface height (SSH; called zos), mixed layer depth (MLD; called mlotst).
Model name Atmosphere nominal Ocean nominal resolution resolution Group 1 (LR-ocea/atm) CNRM-CM6-1-LR 2. 5 • 1 • ECMWF-IFS-LR 0. 5 • 1 • EC-Earth3P-LR 1 • 1 • HadGEM3-GC31-LL 2. 5 • 1 • Group 1* (LR-atm) CMCC-CM2-HR4 1 • 0. 25 • MPI-ESM-1-2-HR 1 • 0. 4 • Group 2 (HR-ocea/atm) CNRM-CM6-1-HR 0. 5 • 0. 25 • ECMWF-IFS-HR 0. 25 • 0. 25 • EC-Earth3P-HR 0. 5 • 0. 25 • HadGEM3-GC31-HM 0. 5 • 0. 25 • Group 2* (HR-atm) CMCC-CM2-VHR4 0. 25 • 0. 25 • MPI-ESM-1-2-XR 0. 5 • 0. 4 • Variables used: tos, tauuo, tauvo, zos, mlotst 2 Data and methodology.
Observational and reanalysis products Several observational and reanalysis data sets are used in the present analysis (see Table  2  for details) in order to evaluate model results' realism in simulating the CUS. For SST, we use the monthly HadISST. 2 data set, which was developed at the Met Office Hadley Centre for Climate Prediction and Research  (Titchner and Rayner, 2014)  We have also used the version 2 of the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (OISST-v2) analysis  (Reynolds et al. , 2007). The OISST analysis combines Advanced Very High Resolution Radiometer (AVHRR) satellite data and buoy-and ship-based observations from the International Comprehensive Ocean Atmosphere Data Set (ICOADS) database  (Worley et al. , 2005).
ERA5 is the latest climate reanalysis, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF;  Hersbach et al. , 2020). The NOAA-20CR v3  (Slivinski et al. , 2019)  datasets are supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the US Department of Energy. Because of a lack of wind stress observations and reanalysis covering the entire domain and period of our study , the surface wind speed is converted into wind stress following an empirical method (see Appendix A for more details). Note that this offline computation of the wind stress is only performed for the observations and reanalysis wind data A. Sylla et al. :  Impact of increasing resolution on the CUS in climate models sets but not for the model, which directly provides the wind stress field.
Thus, gridded data sets at different resolutions may lead to different estimates of the observed Ekman transport. This sensitivity of the Ekman transport to the spatial resolution is therefore crucial to properly compare modeled and observed upwellings. Figure  4  shows the seasonal cycle of the Ekman pumping (W ek ). Focusing on the validation data sets (left panels) and over the IP, W ek tends to be weak or practically zero from October to June, and it is more intense (around 0. 5 m 2 s -1 ) during the summer season in CCMP and ERA5, as found by  Alvarez et al. (2008). For NOAA-20CR-v3, this seasonal cycle is less marked. Along the MoUS, W ek is different in the nMoUS and sMoUS subregions. In the sMoUS, W ek is weak but positive, with maximum values occurring during winter and spring. In the nMoUS, validation data sets show mostly negative values of W ek throughout the year, as found by  Lathuilière et al.
The onshore geostrophic flow and the quantitative assessment of the upwelling rate As discussed in Sect. 2, the effect of T geo is quantified here for the CUS. We have firstly examined the monthly climatology of the meridional sea surface height gradient from the AVISO satellite data and the GODAS reanalysis (see first two columns in Fig. B1  of the Appendix B). The SSH gradient observed over the IP (panel a) is indeed negative all year, thus potentially inducing an onshore geostrophic flow. The maximum and minimum amplitudes of this SSH gradient are found in summer and winter respectively. Over the MoUS (panels b and c), this gradient is negative all year as for the IP and reaches its maximum from May to November in the nMoUS and from July to September (August to October) over the sMoUS in AVISO (GODAS).
The thermal upwelling indices To address the model analysis, we compare UI sst from the observational data sets (Fig. 2 , left panels) and the different model configurations (Fig. 2 , right panels). In the IP, there is a general agreement between observations and models. Models broadly reproduce the climatological seasonal cycle obtained in observations with a maximum in summer. One exception can be noted: the CNRM-CM6 family shows no signature of upwelling with unrealistic negative values of UI sst during summer. In general, the amplitude of the seasonal cycle is slightly enhanced when both the ocean and atmosphere resolutions are increased (comparison among groups 1 and 2). Along the nMoUS and sMoUS, group 2 provides a more realistic representation of this SST index than their LR versions (group 1).