dataesr/scientific-paragraphs-categorization huggingface dataset:
peratures, water or nitrogen supply yield similar strong differences when it comes to model sensitivities. Model evaluation should advance to including emergent functional relationships that may tell more about model plausibility and skill than only comparison with yield data from observations. For this, existing knowledge needs to be collected, tested for generalizability, and translated into simple tests that models can be subjected to. Modelling protocols need to be designed to enable such functionality tests rather than only comparisons with yield data. A community effort is needed to bring together knowledge collection and formalization, model experiment design and model testing in order to advance crop modeling towards reduced uncertainty in crop model applications. 6 Open Research The simulation outputs of GGCMI Phase 2 output variables that we analyze here are available on zenodo.
16S rRNA and 16S rRNA gene sequence analysis Sequence data were analyzed using QIIME2  [46]. All paired-end sequences with quality scores were compressed and denoised using the DADA2 plugin  [47]. The denoising step dereplicated sequences, filtered chimeras, and merged paired-end reads. The truncation parameters to use with the DADA2 plugin were determined using FIGARO  [48]. FIGARO analyzes error rates in a directory of FASTQ files to determine the optimal trimming parameters for sequencing pipelines that utilize DADA2. The truncation length was set to 123 F and 162 R for all the data, with minimum overlap set to 30 base pairs, which resulted in 93% merging success. All truncation was performed from the 3' end for consistent final read lengths. The DNA and cDNA datasets were separately denoised.
Designating the active community members All downstream analyses were performed in R version 4. 0. The R package decontam  [50]  was used to determine the number and identity of contaminants in the dataset (Figures  S3A  and S3B ) and remove them using the prevalence method. Contaminating taxa, mitochondria, and chloroplast sequences were filtered from the datasets. Based on rarefaction curves, a subsampling depth of 15,000 reads per sample was selected (Figure  S3C ). After subsampling, 16S rRNA to rRNA gene ratios (hereafter, 16S rRNA:rRNA gene) were computed from the DNA and cDNA datasets as described in  [20]. While we compared a few methods therein for this dataset (please see Supplemental Information for details), we ultimately chose the method that applied a 16S rRNA:rRNA gene ratio threshold >=1.
Contrasts were set to "sum" before running the ANOVA model to ensure that type III ANOVA tests were valid. We used non-parametric Kruskal-Wallis tests (Kruskal. test in R) to calculate differences in classlevel relative abundances between treatments. We used the indicspecies package in R to determine indicator species associated with experimental conditions. We used the abundance-based counterpart of Pearson's phi coefficient of association within the multipatt function  [53]. To correct the phi coefficient for unequal group sizes the "func" parameter within multipatt was set to "r. g. " P values were adjusted for false discovery rates. Indicator species were calculated at the OTU level for each treatment combination and the OTU and family level for the cropspecific indicators. We created a heatmap visualization of the 50 most abundant and active OTUs in the bean dataset for indicator species.
Rhizobiome sequencing summary: total and active bacteria communities For the total community (DNA) data, total non-chimeric merged reads for both bean and switchgrass were The microbiome data presented, henceforth, all pertain to the DNA counts of the active community members (16SrRNA:rRNA gene >= 1). The percent active taxa ranged from 40-60% of the total observed taxa in a sample. This establishes that an estimated half of the taxa detected in the DNA dataset were not observed or did not meet our activity criterion in the RNA dataset.
Materials and methods In this section we first describe the large datasets used for our N budget (Table  1 ). We also present the individual parameters used as inputs for our modeling (Table  2 ). In both cases we further discuss the uncertainties in Appendix A. From these, a N mass balance is performed for each of the 6 French water agencies basins which are then gathered to obtain the national balance. We also describe our method to estimate the domestic protein consumption that could be covered by reusing all excretions as fertilizers (Table  3 ). The results are part of a larger project assessing nutrient flows in the French sanitation system; the code to generate, cleanup, and analyze the data is available at https://codeberg. org/TStarck/N_P_France_sanitation_system. The original datasets sources before cleanup, the consolidated data used for the analysis, the graphs and more information are available at https: //zenodo.
Data corrections The datasets for sludge production, industry discharges and WWTPs inflows and outflows were screened for obvious outliers. For each of the 6 water agency basins, we aggregate the N flows at the basin scale for each year and look for unexpected peaks on specific years. This allowed to spot obvious outliers. When it was possible to identify that the peak was simply due to a misplaced comma for that particular year (factor 10, 100, or 1,000), we corrected the entry; otherwise, the outlier was not considered in the analysis and the entry was discarded from the final dataset.
Nitrogen in and out of wastewater treatment plants There are more than 20,000 WWTPs in France. We gathered data about the annual mean N inflows and outflows for each WWTP, based on data provided by the 6 French water agencies. The temporal availability of the data varies for each basin, but spans at least one decade (e. g. , 1992-2020 for Artois-Picardy, 2009-2020 for Rhone-Mediterranean) except for the Seine-Normandy basin, where data was only available for the years 2015, 2018 and 2020. For this basin, we retrieved additional data from the SIAAP (Syndicat Interdépartemental pour l'Assainissement de l'Agglomération Parisienne), the syndicate in charge of 6 of the largest WWTPs in the Paris region, which handle half of the basin's pollution. The SIAAP data provides partial data for 2007-2020.
Sludge production and destination Annual sludge production by WWTPs and their destination (direct spreading, compost, incineration, landfill) is reported for each facility in the French sanitation portal database  (Portail Assainissement Collectif, 2023) , and at the national scale in Eurostat  (Eurostat, 2022). Total sludge production p t is consistent over the whole period, around 1-1. 1 Mt/y. However, the sum of the reported destination quantities r t = i d i does not cover the whole production (only 0. 9-0. 95 Mt were reported in 2018-2021 across all destinations). Therefore, we extrapolate the total amount of sludge sent to each destination i from the total production quantities, d i p rt , considering that the fate of missing sludge follows the same distribution as the reported quantities.
, 2019; data. gouv, 2021) , detailed by age and sex categories. We convert the protein contents to nitrogen with a 1/6. 25 factor  (Maclean et al. , 2003). We consider no N accumulation in adult bodies and consider that all ingested N is excreted  (Leach et al. , 2012). N excreted out of the sanitation systems has been neglected (diapers, skin and breath volatilization, open urination). Combining both datasets gives the N excretion for each water agency basin and at the national scale. This results in an averaged national N excretion of 4. 6 kgN/cap/year. More information and a discussion of the uncertainties can be found in section Appendix A. 5.
Funding Thomas Starck was awarded a PhD scholarship from École Polytechnique and Ecole des Ponts Paristech. This project has received funding from the European Union's Horizon Europe research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 101063239 awarded to Tanguy Fardet. The OCAPI Program is funded by several public institutions (www. leesu. fr/ocapi). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements We thank the water agencies and the  French Ministry of Ecological Transition  for providing us with additional data that was not available in the French open-data repositories. We also thank  Gilles Billen ,  Josette Garnier ,  Julia Le Noë ,  Mathilde Besson , and  Etienne Paul , for their advice and helpful discussions. We thank the partners of  OCAPI program  for their support. The Sankey diagram (Figure 1) was made using the Open-Sankey tool https://open-sankey. fr. On the Sankey diagram, the wastewater treatment plant icon is adapted from Daan&apos's work; other icons were made by  Luis Prado ,  Marco Livolsi ,  Jae Deasigner ,  Singlar ,  Gan Khoon Lay ,  WEBTE-CHOPS LLP ,  Trevor Dsouza ,  Elvn Sands  and sandra from the Noun Project: https://thenounproject. com.
Dataset sources. 2. 1. 1. Data corrections. 2. 1. 2. Nitrogen in and out of wastewater treatment plants. 2. 1. 3. Sludge production and destination. 2. 1. 4. Large industries discharge in sewers. 2. 2. N-budget parameters. 2. 2. 1. Nitrogen excretion (urine and feces) from population. 2. 2. 2. Individual Autonomous Systems. 2. 2. 3. N in sludge (N:P ratio). 2. 2. 4. Direct discharge losses. 2. 2. 5. Residual pollution. 2. 3. Potential for food domestic consumption. Contents 1. Introduction 2. Materials and methods 2. 1. 3. Results 3. 1. French sanitation system N budget and recycling. 3. 2. WWTP removal efficiency through space and time. 3. 3. Current agricultural potential.
Farm sample and data collection To establish the farm sample, we sought farms whose farmers were motivated to reflect on relations between their production strategy and its environmental impacts, and who could provide the large amount of data necessary for LCA. We included only farms with dairy or suckler beef cattle (the most common ruminant systems in our region) and intended to select no more than three of each, given the time and effort needed to perform a full LCA of each one. We first enlisted experimental farms known to us (operated by INRAE or the Chamber of Agriculture of the Pays de la Loire region), and their farmers helped identify other potential farms (commercial or experimental) to include. We aimed to select farms that would represent their type of system well (e. g. agricultural rewilding, dairy or suckler beef system).
Life cycle inventories 2. 3. 4. 1. MEANS InOut software. Life cycle inventories (LCIs), which list and quantify the flows of resources used and pollutant substances emitted by the system  (Jolliet et al. , 2015) , were modelled using the web-based software MEANS InOut v3. 5, which facilitates and streamlines generation of LCIs of components of farming systems  (Auberger et al. , 2018). For all systems except K, LCIs of each field and each cow category (e. g. heifers, calves, dairy cows, fattening cows) were created. Direct emissions (e. g. carbon dioxide, methane, nitrous oxide, ammonia, phosphate, nitrate, heavy metals, pesticides, eroded soil) from and resource use (e. g. diesel, electricity, natural gas, water) for the production of crops (i. e. permanent grassland, annual and perennial forage crops, cereals and protein crops) and cattle were estimated using emissions factors and models in AGRIBALYSE® v3.
0 models  (Koch and Salou, 2020) , except for nitrate leaching from grassland, soil organic carbon (SOC) dynamics and carbon (C) storage in unmanaged woody biomass. Background processes (e. g. for production of inputs) from ecoinvent v3. 5  (Moreno-Ruiz et al. , 2018)  and AGRIBALYSE® v3. 0  (Koch and Salou, 2020)  databases were used. Due to the unique management strategies and animal products of K, its LCIs were built differently. First, emissions factors of European extensive livestock systems (IPCC, 2019b) for each animal species (i. e. cattle, pigs, ponies and deer) and category (for cattle) were used to estimate direct emissions and manure emissions, without detailing onfarm feed production (only grazing and browsing) or animal feed using MEANS InOut (Table  S1 ). Emissions factors varied as a function of live weight.
7 ha) and the newly-established woodland (35. 2 ha), IPCC (2019a) data were used to estimate the annual rate of C storage. First, total annual biomass growth was estimated from annual aboveground biomass growth and the ratio of below-to above-ground biomass in a natural broadleaf forest in a temperate oceanic European climate. Then, the C fraction in this biomass (assuming that below-ground biomass had the same C fraction as above-ground biomass) was used to estimate annual C storage in all woodland biomass (Table  S7 ). For the three types of shrublands that became naturally established, Czura (2021) estimated the storage of above-ground C at K using allometric equations (one per type) that had been developed in previous studies and LIDAR data from K. The same ratio of below-to above-ground biomass was then used to estimate annual C storage in all shrubland biomass (Table  S7 ).
Energy return on investment Despite having extremely low productivity, K had the highest EROI because it used nearly no inputs. In comparison, dairy systems always had higher (and less variable) EROI than suckler beef systems because producing milk greatly increases the amount of human-edible protein produced from cattle-oriented systems. EROI has rarely been used to assess livestock systems, but it serves as a useful indicator of the energy efficiency of producing agricultural products. For comparison, we calculated the EROI of the seven composite dairy systems in France assessed by  Salou et al. (2017) : highland, organic, grass-based, intensive grass-based, maize-based, intensive maize-based and very intensive maize-based. The highland system represented dairy production in mountainous regions (from the AGRIBALYSE® database), while the other systems were developed from data on 69 lowland dairy systems throughout the country using factorial analysis and hierarchical clustering.
Cloud feedback computations The response of clouds to climate warming, referred to as cloud feedback, used in this study is the same as in Zelinka et al. 1. In that study, they characterize the atmospheric contributions to cloud feedbacks by using model outputs from the CMIP database in which the atmospheric CO2 levels were quadrupled (4xCO2 experiment) compared to a pre-industrial atmosphere (piControl experiment). Then, the cloud feedback is computed using radiative kernels that quantity the sensitivity of top-of-the-atmosphere radiation to small perturbations and is then adjusted for non-cloud in uences  57. Finally, the SW component is further decomposed into the amount, scattering and absorption sub-components using the approximate partial radiative perturbation (APRP) technique 58. Because the absorption component is very small compared to the scattering, we treat the scattering component as the sum of the scattering and absorption components.
For clarity, no observations are shown at temperatures greater than 0˚C. The insets show the temperature at which liquid clouds transition to ice clouds for all different models and observational datasets, expressed as the temperature at which clouds are 50% liquid and 50% ice (LPR = 0. 5, noted T 50/50 ). Note that for the CMIP3 models, only the T 50/50 values are available from the literature (Table  S3 ), which are extrapolated to a narrow band along the 50/50 isoline. Figure 2 Relationship between temperature and LPR in three generations of CMIP models against satellite observations. Mass (as in Fig. 1a ; dashed) and frequency (solid) LPR in CMIP models (red, purple and pink for CMIP6, CMIP5 and CMIP3 models, respectively) against observed CALIPSO-GOCCP frequency LPR (as in Fig. 1b ; Cesana et al.
Acknowledgements GC and AA were partly supported by a  CloudSat-CALIPSO RTOP  at the  NASA Goddard Institute for Space Studies. IS and GC were supported by  DOE  grant  DE-SC0021004. GC was also supported by  NOAA  grant  NA20OAR4310390. AA and AF were supported by the  NASA   Modeling, Analysis, and Prediction Program. Contributions of IS and AF were also supported by  DOE  grant  DE-SC0018046. MDZ's work was supported by the  U. S. Department of Energy (DOE) Regional   and Global Modeling Analysis program  area and was performed under the auspices of the  DOE  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344. Resources supporting this work were provided by the  NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. We thank  NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center  for providing computing resources to run  GISS ESM ,  NASA  and  CNES  for giving access to CALIPSO observations, and  Climserv  for giving access to CALIPSO-GOCCP observations and for providing computing resources.
Data availability The CALIPSO-GOCCP observations (Cesana et al. 29  ) can be downloaded from the CFMIP-Obs website (http://climserv. ipsl. polytechnique. fr/cfmip-obs/Calipso_goccp. html). The CMIP ESM outputs were downloaded from both the ESGF (https://esgf-node. llnl. gov/) and climserv websites (https://climserv. ipsl. polytechnique. fr/). The CMIP cloud feedbacks used in Fig. 3   (Zelinka, 2021) are available at https://doi. org/10. 5281/zenodo. 5206851. The GISS-ModelE3 data to reproduce Fig. 3d  will be added to zenodo if the manuscript is accepted.
The same methodology of analysis and data treatment was employed, producing a unique dataset for the investigation of molecular composition of organic matter in cloud water. Besides the analysis of elemental composition, we investigated the carbon oxidation state (OSC) of dissolved organic matter, finding that overall samples collected at PUY are more oxidized than those collected at REU. Molecular formulas were also classified based on stoichiometric elemental ratios, showing the high 35 frequency and abundance of reduced organic compounds, classified as lipids (LipidC), in this matrix, which led to search for terpenes oxidation products in cloud water samples. To better discriminate between samples collected at PUY and at REU, statistical analysis (principal component analysis and agglomerative hierarchical clustering) was performed on the ensemble of molecular formulas and their intensities.
1±10. 5 weighted percentage), as reported in Figure  1. Consequently, the CHO contribution was 36. 4±12. 6 in percentage in number, higher than the value observed for REU samples, while the weighted percentage, of 41. 1±10. 4, was not different. Nevertheless, these differences are not significant for CHO (p≥0. 05) and may be significant for CHNO (p=0. 058). A larger dataset is needed 280 to draw conclusions on this point. Conversely, the contribution of CHOS was significantly higher (p<0. 01) in REU samples (13. 5% in number, 14. 7% in weight) than in PUY samples (6. 3% in number, 6. 5% in weight). One explanation is the possible influence of fresh marine emissions at REU that can be responsible for the emission of organo-sulfate compounds  (Wang et al. , 2023). Similarly, the CHNOS were significantly lower in samples collected at REU (p<0.
, 2015). Figure  2a  depicts 290 the OSC for samples collected at REU (in yellow) and at PUY (in blue). OSC is a parameter that always increases with oxidation and was studied to investigate oxidation of DOM in aerosols, rains and fogs. Commonly, reported measurements by ESI of water-soluble organic carbon (WSOC) indicate OSC values lower than -0. 9 for aerosol (in red in Figure  2a ), between -0. 9 and -0. 7 for rainwater (in green) and higher than -0. 7 for fog water (in purple)  (Kroll et al. , 2011). This increase in the OSC suggests additional oxidation of WSOC in the aqueous phase. The ANOVA test, a statistical tool for the analysis of the 295 difference between the means of more than two datasets, was used to investigate the differences of OSC between PUY and REU samples, as reported in Figure  S2.
R8 and R9 are similar whereas they are significantly different from R10B. Nevertheless, the three samples collected at REU are not significantly different from autumn samples collected at PUY (22/10/2019, 03/11/2020, 08/10/2021), with the exception of 02/10/2019. PUY dataset presents more heterogeneous values than REU sample set, with a clear seasonal variation: winter samples have a lower OSC than summer and autumn samples, 300 respectively of -0. 97±0. 56 (winter), -0. 75±0. 65 (autumn) and -0. 60±0. 58 (summer). This can be explained by the lower photoreactivity and the lower temperature during winter that decreases the efficiency of oxidation processes. The measured OSC for PUY autumn samples is not significantly different from the one measured at REU -0. 77±0. 55 (p>0. 05); nevertheless, the environmental conditions are not comparable.
05). Nevertheless, the question on the high frequency and abundance of LipidC raises many questions, especially because  (Renard 365 et al. , 2022)  found almost the same abundance of LipidC for clouds collected under different environmental conditions (PUY) and analysed using a different FT-ICR MS ionization source (i. e. , atmospheric pressure photoionization (APPI)). LipidC contains a broad variety of organic compounds, from long chain carboxylic acids to sterols. Intuitively, there is more chance to detect long chains carboxylic acids than sterols in cloud water, but it is difficult to set-up a more systematic approach to investigate LipidC category only studying MFs. To discriminate lipids into categories, we used the LIPID MAPS ® database. 370 This lipid classification system comprises eight lipid categories: fatty acyls, glycerolipids, glycerophospholypids, sphingolipids, sterol lipids, prenol lipids, saccharolipids and polyketides  (Liebisch et al.
The matrix is composed of 9251 scores (molecular formulas) and 10 loadings (3 samples from REU and 7 for PUY). Unfortunately, the small dataset does not enable to find groups of MF typical of PUY or of REU: a larger number of samples is needed for this analysis. 480 We chose the representation of the results in a 2-D plot, although two principal components (PCs) are not enough to explain more than 48. 7% of the variance of the dataset. In fact, to have a complete description of the variance (>85%), we need to extract at least 6 PCs (86. 2%). The table of loadings, in Table  S5 , shows the correlation between the first six PCs and the samples (loadings). Samples collected at REU are mainly correlated with PC1, with values higher than 0. 5, uncorrelated with PC2 (-0. 2) and mostly not correlated with the other PCs.
Conclusions 510 In this study, we present for the first time the molecular characterization of three cloud samples collected in the Southern Hemisphere during the BIO-MAIDO field campaign, in the Réunion Island (REU) (Indian Ocean). Cloud water samples were pre-concentrated by solid phase extraction and analyzed by FT-ICR MS in electrospray negative ionization mode. Mass spectra were treated with Data Analysis and the assignment was performed with MFAssignR software. The composition of samples collected at the Reunion Island was compared to those of samples collected at puy de Dôme (PUY), in the Massif Central 515 region of France. The same methodology was used for sampling, analysis and data treatment (assignment), in order to obtain a unique database of highly comparable samples. The average elemental composition is similar for the two samples dataset: nevertheless, REU samples present a higher occurrence of CHOS and a lower occurrence of CHNOS compared to PUY samples.
Acknowledgments Access to the  CNRS research infrastructure Infranalytics  ( FR2054 ) is gratefully acknowledged. The authors gratefully 560 acknowledge the team working on BIOMAÏDO field campaign. Thanks to the technical and logistical support provided by " Observatoire des Sciences de l'Univers de la Réunion"  and " Laboratoire de l'Atmosphère et des Cyclones". ACTRIS France  is also acknowledge for facilitating the access to national facilities, such as the Maïdo observatory.