dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical tests The SPSS 20. 0 software allowed to perform the non-parametric H tests of Kruskal Wallis and the Mann Whitney test. These tests were performed to show the influence of abiotic variables on the abundance of study organisms between sampling stations. Spearman correlations were used to show the relationships between abiotic variables and the different taxa surveyed. However, before performing these tests, normality of the data was sought using the Kolmogorov-Smirnov test.
In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics' highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain.
To this end, our approach was (i) to analyze the identifiability of some compartmental simplifications of the GreenLab model for tomato plants in the case where the data set consists only of environmental variables, (ii) to derive from the GreenLab principles a new family of models focusing on water consumption as the main state variable and differing by their assumptions regarding the dynamics of the LIR, and (iii) to perform a very preliminary comparison of these models using an experimental dataset of water consumption by tomato plants. Although mostly theoretical at this stage, this work has some conceptual interest in presenting an original stochastic approach to derive a new class of simple models and providing a procedural guideline to further confrontations to experimental results.
Data collection Between May 10 and July 2, 2021, an extensive study was conducted in a hydroponic greenhouse near Therma village, within the Nigrita-Serres region  (40. 91, 23. 55) , Greece, to examine the tomato plant's (cv. ecstasis) water consumption patterns. A drip irrigation system was used to ensure precise irrigation for each individual plant. Rockwool was used as a substrate growing medium, a product of basalt mainly composed of Oxide of Calcium (CaO) with small percentages of Iron (Fe) and Aluminum (Al), in keeping with common practices in the region. Plants' density is reported as 5 stems per m 2 (one stem per plant). Indoor measurements were performed using an Efento Logger  (Efento, 2020). Additionally, meteorological data were collected using a Davis Vantage Pro 2 (Plus) weather station close to the greenhouse.
Model comparison and prediction performance criteria To have a challenging baseline model to compete with, we first estimated a linear first-order autoregressive model with one exogenous variable, namely the average solar radiation received at day t, E(t): W c (t) = b 0 + b 1 W c (t -1) + b 2 E(t) + e t , ( 1 9 ) where the b i coefficients are estimated via the ordinary least squares method. In terms of forecasting, a sequential methodology is employed. From the original dataset, we initially extract the first 55% days (days 39 to 68) for training and predict the next day's water consumption (day 69). Subsequently, we increase the size of the training set by one additional day at each step, continuing to predict the following day until we reach the end of the time series. The parameters are reestimated at each step of the procedure, using a total of 1000 distinct starting points in our calculations, subsequently selecting the point with the highest likelihood value as the model's parameter for prediction.
, 2014)  or crop models of tomato [e. g. Marcelis et al. (2008) ]. Various strategies for using a mechanistic model in such a setting can be considered, each with a unique potential role. The first strategy revolved around employing GreenLab as a completely prefitted model, as done in  Chew et al. (2014)  for the case of Arabidopsis. However, the issues here concerned uncertainties and discrepancies among various genotypes. The second strategy would be to estimate all the parameters of GreenLab, but due to the incomplete dataset and the current experimental protocol, this strategy proved infeasible as the model exhibited identifiability problems, similar to the ones reported in  Letort et al. (2012)  for Coffea trees when only compartment data are available. Such an estimation procedure would necessitate destructive plant data, at different growth stages, for estimating the sink strengths and their variations  Guo et al.
(2018) ;  Bhaskara et al. (2022) ]. Therefore, using a constant value is likely to be valid only in a limited range of environmental conditions that would have to be determined using a more extensive experimental dataset  (Lanoue et al. , 2017)  All these simplifications were required with respect to our objectives and our context of using only routinely recorded variables. They can, however, be considered applicable when describing the average growth of plants in standard conditions, and most of them are also laid in other models  [Ma et al. (2022); Winn et al. (2023) ]. An additional underlying assumption that deserves to be highlighted is that the g function is time-independent. In reality, g aggregates the effects of blade spatial arrangement, which determines the probability of a radiation ray being intercepted, the fraction of biomass allocated to the blades and the senescence of the leaves.
Conclusion In this study, we aimed to better understand plant water consumption, a subject of considerable importance for greenhouse management. The widely-used GreenLab model was not identifiable in our setting, even after compartmental simplifications, but it could be considered in other applications if at least partial information on the plant could be collected. Using similar physiological assumptions but in a probabilistic framework, we introduced the SSiE model as an alternative, directly applicable to water consumption, thus avoiding the need for biomass production as an intermediary variable. Despite the limitations of our data, the SSiE model provided some useful preliminary insights, particularly in the area of light interception over time. While these findings are still at a mostly theoretical stage, our proof-of-concept on our experimental dataset hints at the SSiE model's potential utility for water consumption and light interception analyses.
METHODS This study uses individual EHR data stored in the N3C Data Enclave as part of the NIH Researching COVID to Enhance Recover (RECOVER) Initiative. The RECOVER Initiative seeks to understand, treat, and prevent PASC. For more information on RECOVER, visit https://recovercovid. org. The N3C Data Enclave provides access to harmonized EHRs from more than 75 health sites with data from over 16 million patients. 21, 22  We used N3C data from version 87 (8/2/2022), which has 63 contributing sites, for the current investigation. The N3C Data Enclave's Palantir Foundry platform (2021, Denver, CO), a secure analytics platform, was used for data access and analysis.
Characterization of Reinfection We used two approaches to characterize reinfection. The first is a cohort summary where we calculate summary statistics related to reinfection and disaggregate by demographic characteristics. Chi-square tests were used for categorical variables and Student's t-test or ANOVA were used for continuous variables (Table  1, Supplementary Table 2, 3 ). Time to reinfection analysis was based on Kaplan-Meier curves from the survival package in R. This analysis was performed using the date of the COVID-19 index date (the date of earliest diagnosis or positive test) and the date of event (first reinfection date) as endpoints. The second approach to characterizing reinfection is with biomarkers. We explored the trajectories of various biomarkers around COVID-19 initial and subsequent index dates from patients with and without a reinfection.
Biomarker measurements included laboratory values of ferritin, fibrinogen, C-reactive protein, procalcitonin, white blood cell count, absolute lymphocyte count, absolute neutrophil count, erythrocyte sedimentation rate, albumin, D-dimer, alanine transaminase (ALT), aspartate transaminase (AST), and serum creatinine. Units were harmonized across data partners, and clinically infeasible values were excluded according to standard N3C data quality protocols. 22  Measurements were taken from 100 days prior to and 180 days after the COVID-19 index date in patients with and without reinfection. The same time frame was used for collection around the first reinfection index date in individuals with at least one reinfection. Laboratory values were reported separately for hospitalized and nonhospitalized patients.
Characterization of Reinfection Table  1  describes the study cohort and highlights differences between the subgroup with no reinfections and the subgroup with at least one reinfection. A total of 5. 9% of the study cohort had at least one documented reinfection. A documented reinfection was defined as a positive SARS-CoV-2 PCR or antigen test that occurred 60 or more days after a COVID-19 infection index date. Home COVID-19 tests administered outside a healthcare setting were not included in the dataset. The subgroup of reinfected patients tends to be younger and more likely to have documented race and ethnicity information. Although the majority of individuals (N=89,357) had only one reinfection, a small group (N=102) had three or more reinfections. This group was characterized by non-Hispanic, male, White race, and older age groups compared to other groups (Supplementary Table  1 ).
Strategies we have used to address these limitations include hospitalization inclusion criteria for sites and visit inclusion criteria for individuals that promote more detailed, robust, and higher quality data. A second limitation is that it is not feasible to include results of home COVID-19 tests. Individuals may be testing positive for COVID-19 reinfections that are not documented in this dataset. This may result in an underestimate of the number of individuals with reinfections. The varying availability of home tests over the duration considered for this project may result in an uneven impact of this limitation. To address this limitation, we have attempted to maintain a focus on behaviors that require healthcare interaction, such as Long COVID diagnosis, biomarkers, and hospitalization.
ACKNOWLEDGMENTS Authorship was determined using ICMJE recommendations. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or N3C or RECOVER. This study is part of the  NIH   Researching COVID to Enhance Recovery (RECOVER) Initiative , which seeks to understand, treat, and prevent the post-acute sequelae of SARS-CoV-2 infection (PASC). For more information on RECOVER, visit https://recovercovid. org/. This research was funded by  National Institutes of Health (NIH)  Agreement  OTA OT2HL161847  as part of the  Researching COVID to Enhance Recovery (RECOVER) research program. The  Data Use Request  (DUR) ID is  DUR-94BBC49. We would like to thank the  National Community Engagement Group (NCEG) , the Patient- Led Research Collaborative (PLRC) ; all patient, caregiver, and community Representatives; and all the participants enrolled in the  RECOVER Initiative.
ADNI-3 Cohort The ADNI-3 cohort was used for validation. The data used in the preparation of this paper were partly obtained from the ADNI database  [14]. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W Weiner, MD. The primary goal of the ADNI was to test whether serial magnetic resonance imaging (MRI), PET, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early Alzheimer's disease (AD). The ADNI was approved by the Institutional Review Boards of all participating institutions, and written informed consent was obtained from all participants at each site. According to our study aim, we selected subjects in the ADNI phase 3 (ADNI-3) who completed both the Trail Making Test.
Data Selection and Preprocessing The flowchart of participant recruitment for the CN-NORM cohort is shown in Figure  S1  in Multimedia Appendix 1. After application of inclusion criteria and integrity filtering, 871 participants were retained and included in the analysis. Most missing data were from the MCI or CN group, who were capable of completing the tests. However, due to computer recording, equipment problems, or the subjects being tired and not completing the tests, these data were missing. The mean value method was used to fill in the missing data.
The Hosmer-Lemeshow (H-L) test was used to determine the χ 2 goodness of fit of each model. A 2-sided P value of <. 05 was considered statistically significant. Python (v3. 8), scikit-learn (v0. 24. 1), scipy (v1. 5. 2), statsmodels (v0. 13. 2), matplotlib (v3. 3. 4), seaborn (v0. 11. 1), and SPSS version 20. 0 (IBM) were used for data analysis and visualization.
The feature selection results regarding discrimination between the MCI and CN groups are shown in Figure  3   S6  in Multimedia Appendix 1). Model 1 covered 3 domains: language, memory, and executive function (Figure  4B ). The top 5 variables, selected using SelectKBest and the AUC LR methods, were similar (Table  S7  in Multimedia Appendix 1 and Figure  3 ); the other variables declined sharply in discriminative.
Acknowledgments We thank all the researchers who helped with the data collection in the CN-NORM cohort. We also thank all the participants and their family members for their time and involvement in the CN-NORM cohort. This work was supported by the  National Key Research and Development Program of China  (grants # 2018YFC1314200  and # 2017YFC1311100 ), the  Science and Technology Innovation 2030-Major Project  (grant # 2021ZD0201805 ), and the  National Natural Science Foundation of China  (grant # 82271584 ). The funders were not involved in the study design, data collection, data analysis, and data interpretation, nor did they play a role in the writing of the manuscript and the decision to submit it for publication. The Alzheimer's  Disease Neuroimaging Initiative (ADNI)  data collection and sharing for this project was funded by the  ADNI (National Institutes of Health  grant # U01 AG024904 ) and the  Department of Defense  (award # W81XWH-12-2-0012 ).
The  Canadian Institutes of Health Research  provides funds to support ADNI clinical sites in Canada. ADNI investigators contributed to the design and implementation of the ADNI database or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available [29]. Private sector contributions were facilitated by the  Foundation for the National Institutes of Health. The grantee organization was the  Northern California Institute for Research and Education , and the study was coordinated by the  Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the  Laboratory for Neuro Imaging at the University of Southern California.
While there was no significant improvement between the CMIP5 and CMIP6 results  (Roussel et al. [2020] ), the reliability of the precipitation change in future scenarios is questionable and there is an urgent need to evaluate and improve the models. The evaluation of models requires reference observational data and a well defined evaluation set-up. Satellite data are very useful to study the atmospheric column : thanks to the measurements from the Cloud Profiling RADAR (CPR) onboard the CloudSat satellite, the first model-free surface snowfall climatology over Antarctica have been constructed  (Palerme et al. [2014] ,  Palerme et al. [2019] ) then further expanded by adding the vertical component in  Lemonnier et al. [2020]. These dataset make it possible to assess some spatial and temporal characteristics of the vertical distribution of antarctic precipitation.
However they have important limitations, such as the long revisit time, a short period of operation (from 2007 to 2010), and the absence of measurements below 1200 meters (so-called blind zone,  Maahn and Kollias [2012] ), where the most intense snowfall occurs and where the sublimation by katabatic winds takes place  (Grazioli et al. [2017b] ,  Durán-Alarcón et al. [2019] ,  Vignon et al. [2019b] ). The Year Of Polar Prediction in the Southern Hemisphere (YOPP-SH) campaign which took place between 2017 and 2019 is a unique opportunity to carry out an in-depth evaluation of models thanks to the large amount of observational data collected and the simulations provided by climate and weather prediction centers. The YOPP was part of the Polar Prediction Project (PPP) of the World Weather Research Program (WWRP) initiated by the World Meteorological Organisation (WMO) whose main objectives were i) to improve the meteorological observations at the poles, ii) to enhance the prediction capabilities of Numerical Weather Prediction (NWP) models at high-latitudes,.
| Data from atmospheric models and meteorological reanalyses This section presents the different models involved in the evaluation. Detailed information concerning the horizontal, vertical and temporal resolutions of the datasets used is given in Table  S. 1. We have analysed output from individual weather forescasting models (2), atmospheric circulation models (2) and reanalyses (2) which where freely accessible for the time period of our study. Since we simultaneously study outputs of forecasting models (with a forecasting time step), quantities calculated from analyses of forecasting models, as well as models nudged towards continuous reanalyses, we must pay attention to the temporal dimension of the data compared. The daily totals of the different models (computed using UTC times) will be analysed and precipitation occurences will be seen as forecast hereinafter, whether they are predictions from weather forecasting models or outputs from nudged climate models.
ARPEGE-SH ARPEGE (Action de Recherche Petite Echelle Grande Echelle) is a global hydrostatic model of NWP (e. g. Pailleux et al. [2014] ). It is developed by Meteo-France and used for weather forecasting. The model has sometimes been used for Antarctic studies  (Ricaud et al. [2020] ). The cloud microphysics of ARPEGE has four pronostic variables for precipitation (cloud ice and water, solid and liquid precipitation). ARPEGE-SH is the dedicated configuration built for the YOPP-SH campaign. This specific configuration of the operational model is used with a higher resolution (around 7. 5km) over Antarctica (with a zoom centered on Dome-C, 123. 3E 75. 1 S, East Antarctic Plateau) than for the NWP operational version over France (see Table  S. 1). A 4DVar assimilation was performed every 6 hours with the in-situ (radiosondes, aircrafts, ground-based stations, buoys and ships datasets) and satellite observations used by the operational version of ARPEGE.
IFS The IFS (Integrated Forecasting System) model is a global NWP model developed at the European Centre for Mediumrange Weather Forecasts (ECMWF). Its cloud microphysics scheme is described in ECMWF  [2021]. The ECMWF contribution to the YOPP dataset archive consists of two different products that have been made available for the whole campaign : one operational ensemble medium-range forecast -performed at around 18km resolution with hourly outputs of selected meteorological fields from IFS -and one dedicated short-range research run which is similar to the operational control forecast of the ensemble run. In this work we evaluate the second product for which we could access only the daily averages from the original forecasts. The YOPP dataset can be founded on the ECMWF website (ECMWF  [2017] ) and it is presented thoroughly in  Bauer et al.
ERA5 ERA5 is a global reanalysis of the atmosphere produced by the ECMWF based on the IFS model (version CY41R2) and its data assimilation system  (Hersbach et al. [2020] ). Numerous atmospheric, land and oceanic variables are computed on a 30 km resolution grid. Since 2019, ERA5 supercedes the previous ERA-Interim reanalyses dataset -for the whole time period since 1979. For this reanalysis, we computed the instantaneous solid precipitation fluxes by applying the methods used in line in the IFS model -detailed in the description of the model (see S. 3).
MERRA2 The Modern-Era Retrospective analysis for Research and Applications, (MERRA) is a global reanalysis based on the Goddard Earth Observing System Version 5 (GEOS5) Earth System Model -which is a set of atmospheric, ocean and land model components developed by the Global Modeling and Assimilation Office (GMAO) of the National Aeronautics and Space Administration (NASA) -and with an advanced assimilation system including several in-situ and space-based meteorological observations. Since 2016, the second version MERRA2  (Gelaro et al. [2017] ) supersedes the original MERRA dataset, providing data from 1980 on a 50km resolution grid. The dataset used in this study can be founded on the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) website  (GMAO [2015] ).
| Evaluation methodology To assess the prediction quality of the six simulations and reanalyses we evaluated both the accuracy of the timing of the precipitation occurences and that of the amount of snowfall during the campaign. At the ground surface, the reference used for total snowfall is the data from the snow gauge OTT PLUVIO2. The surface solid precipitation was extracted from modeling datasets to perform the surface comparison. Along the vertical, the reference observational dataset is that from the MRR, which is compared to the profiles of solid precipitation fluxes from the simulations and reanalyses. As all the simulations and reanalyses are not available with hourly timesteps, we chose to make the comparison at the daily time scale. We also choose to consider PLUVIO2 as the daily surface reference to keep a single framework between comparisons of occurrences and quantities.
| Thresholding and classification of days with snowfall Precipitation events are considered at the daily time scale. In order to filter the instrumental noise as well as spurious numerical precipitation in models, it is necessary to determine precipitating days that are considered as significant or non-negligible. The snow gauge having already been filtered out using data from the lowermost MRR gate data, it is considered that any precipitating day with more than 0 mm is not negligible. Also, the manufacturer's manual indicates that a native threshold of 0. 1mm per hour is already taken into account in the raw results of the snow gauge. Across the vertical atmospheric column, it can happen that values measured by the MRR are masked. They are echoes filtered out by the processing algorithm depending on a threshold set to distinguish if the reflectivity is strong enough to be considered.