dataesr/scientific-paragraphs-categorization huggingface dataset:
To reach these uncertainties, the IPSL-CM6A-LR ensemble (green triangles) requires less than 20 members. On average across CMIP6 ensembles (boxplot), less than 30 members are required, while focusing on individual models three models require ensembles with more than 60 members for N3 PR variance and N3 SST skewness. It is also interesting to note that the RES can be three times larger for N3 PR variance compared to N3 SST variance to reach the same relative uncertainty (20%), meaning that the internal variability of N3 PR variance is larger relative to that of N3 SST variance. This is likely linked to the fact that precipitation is more nonlinear, implying stronger interdecadal modulation of its variance. Secondly, one may want to know the sign of the ensemble's bias and set the absolute uncertainty to a confidence interval on the absolute difference between ensemble mean and observational dataset (for the 95% confidence interval, ∆= 𝑃 5 |𝐸 𝑥̅ -𝑜𝑏𝑠|; Figure  5 ; black).
Acknowledgments We acknowledge the  World Climate Research Programme 's  Working Group on Coupled Modelling , which is responsible for organizing CMIP. We thank all the international climate modeling groups for their tireless development efforts, and for generously producing and publishing these coordinated, standardized, and quality-controlled simulations. We thank the  Earth System Grid Federation (ESGF)  for archiving the simulations and improving access, and are grateful to the multiple funding agencies who support CMIP and  ESGF. The U. S.
Therefore, the horizontal axis represents the fraction of the ensemble size used for the computation. The results are presented for epoch lengths ranging from 30 to 150 years (15-year intervals) from the CMIP6-MME and 14 LEs with at least 15 members. We select here larger manuscript submitted to Journal of Advances in Modeling Earth Systems LEs compared to our initial threshold as we are creating synthetic ensembles of a smaller sizes and the minimum size of these synthetic ensembles is 10. There is a total of 188 curves (15 datasets x 9 epoch lengths = 135 for the historical run, and 53 for the piControl run as the ensemble size decreases and fall below the 15 members threshold when the epoch length increases). All 15 datasets align almost perfectly on the theory (dashed black lines) for all three statistical moments computed with N3 PR and N3 SST.
Phytoplankton growth is influenced by nutrient availability, light penetration, and water temperature. In the version of the model we used, river supply of all elements except DIC and alkalinity remains constant across simulations and is obtained from the GLOBAL-NEWS2 datasets  (Mayorga et al. , 2010). For further insights into the model parameterizations, see  (Aumont et al. , 2015). We conducted eight equilibrium simulations, distinguished solely by their respective orbital parameters as shown in Table  1. Four of the simulations that we are using here have already been published  (Beaufort et al. , 2022; Beaufort et al. , 2021). Out of the eight simulations, six were performed at high eccentricity, representing the most extreme values observed during the past million years. These high eccentricity simulations encompassed six distinct angles of precession in order to achieve a 60°resolution ( 2 months) of the perihelion motion in the orbital plane during a precession cycle.
Comparison with modern conditions For comparison with simulations, SST and PP datasets were used to illustrate their seasonality and annual average values. Modern SST data were gathered from the Comprehensive Ocean-Atmosphere Data Set (COADS), which compiles marine observations conducted by ships of opportunity between 1854 and 1992. These SSTs were edited and statistically summarized on a monthly basis for the period of 1961-1992, utilizing 2 degrees in latitude by 2 degrees in longitude cells  (Slutz et al. , 1985). Modern PP data estimates were acquired from MODIS satellite measurements taken between 1998 and 2021  (Slutz et al. , 1985). This dataset provides monthly averaged measurements in grid cells of 7 km2. The data were averaged at a resolution of 1 degree in latitude by 1 degree in longitude, and subsequently, monthly averages were computed over the entire 1998-2021 period.
Seasonality analysis In the following analysis we adopt the basic method of calculating the annual amplitude (e. g. Chen and Yu, 2015)  of SST or PP by determining the difference between the highest and lowest monthly values for each grid point on the dataset, as in  (Beaufort et al. , 2022). We preferred this approach to seasonality indices commonly employed in the field of hydrology sciences to characterize the annual patterns of humidity or river flow -such as the duration of the rainy season, the seasonal ratio, or more intricate methods involving complex histograms (e. g. Laaha and Blöschl, 2006 ) -for its simplicity and robustness.
Acknowledgements. We thank  Anthony Gramoullé  for the help extracting relevant information from model outputs. We thank  Christophe Menkes  for helpful discussions, and  Clara Bolton  for reading an early version of the manuscript. Luc Beaufort  is supported by the  Agence Nationale de la Recherche  ( ANR22 ESDIR 003-ITCH ). Anta-C. Sarr  is supported by a grant from  Labex OSUG  ( Investissements d'avenir   -ANR10 LABX56 ). Anta-C. Sarr  was granted access to HPC resources of TGCC under allocation 2022- A0090102212  made by GENCI. Colored figures in this paper were made with perceptually uniform, color-vision-deficiency-friendly scientific color maps (Crameri et al. , 2020), developed and distributed by  Fabio Crameri  (https://www. fabiocrameri. ch/colourmaps/). 12 https://doi. org/10. 5194/cp-2023-80 Preprint.
12 and 9. 03 nm h -1 for the size range of 7 -20 nm, respectively) and the annual mean GR (10. 31 and 13. 65 nm/h for size ranges of 3-7 nm and 7-20 nm, respectively) reported from the same site in previous studies  (Rose et al. , 2015; Sellegri et al. , 2019). However, due to the limited dataset in our study, this is not necessarily an indication of discrepancy. It is also worth noting that the GR in the previous studies were defined using the maximum concentration method  (Hirsikko et al. , 2005) , i. e. , a different method compared to this study. In the maximum concentration method, the GR is determined by a linear fit to the datapoints of moment of maximum concentration of each size bin, while in the so-called mode fitting GR method used in this study, the GR is obtained by a linear fit to mode geometric mean diameters determined at each moment.
Acknowledgments: This work was supported by the  Academy of Finland Center of Excellence programme  (grant no. 307331 ), the  Academy of Finland Flagship  funding (grants no. 337550  and  337549 ),  H2020   European Research Council  ( CHAPAs , grant no. 850614 ) and  University of Eastern Finland Doctoral Programme in Environmental Physics, Health and Biology. We acknowledge the  IRD  ( Institut de Recherche pour le Développement) delegation in Bolivia  for assisting with logistics and customs clearance for the instruments and the personnel of  IIF-UMSA  for assisting with the station functioning, maintenance and electric troubleshooting. The instrumental deployment at Global GAW station used in this study is supported by an international consortium funded by  IRD ,  Centre National de la Recherche Scientifique (CNRS)  and  Ministère de la Recherche  (under  ACTRIS-FR activities ),  ICOS Network ,  Observatoire de Sciences de l'Univers de Grenoble  (under  Labex OSUG @2020),  Leibniz Institute for Tropospheric Research ,  Consejo Superior de Investigaciones Científicas and University of Stockholm.
Code availability: The code for MODNAG are available at https://doi. org/10. 5281/zenodo. 5592258  (Heitto, 2021)  Data availability: The datasets are available upon request from the corresponding authors. Author contributions: AH, CW, CM and TY designed the study. AH conducted the simulations. CW, DA, LB, XC, RC, YG, LH, WH, PL, KW, AW, QZ, FB and CM provided observational data. FV, IM, MA provided logistic support for the campaign and meteorological data from Chacaltaya. AH, CW, KL, CM and TY analyzed the results. AH and TY prepared the manuscript with contribution from all co-authors.
Groundwater is an important source for drinking water supply, agricultural irrigation and industrial uses in the Middle East and North Africa region. Due to the growing need for groundwater use, groundwater quality studies on the presence of contaminants of emerging concern (CECs) and pesticides have gained attention. The Wadi El Bey is one of the most polluted areas in Tunisia. However, very limited data on CECs infiltration into aquifers has been described, in comparison to industrialized countries where groundwater contamination has been comprehensively addressed. To gain early insight into potential contamination, groundwater wells in northeast Tunisia, an area with high population density and intensive agricultural activity were sampled during two seasons and were analyzed with two high resolution mass spectrometry approaches: target and suspect screening.
In this context, this work investigated the occurrence of 116 CECs and 20 pesticides to help understand the occurrence and potential impacts of CECs in Tunisian groundwater. The watershed of El Bey Wadi and the associated shallow aquifer in Grombalia were selected for this study as they are surrounded by high anthropogenic activities, including agricultural activities and agglomerations. Two sampling campaigns in the Grombalia aquifer can provide valuable data to improve the understanding of the occurrence and distribution of CECs in groundwater samples from Tunisia. Analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-MS) were employed to take advantage of the unique features of high resolution mass spectrometry (HRMS) to identify and quantify the diverse range of CECs and pesticides.
, 2016). The humidity and temperature were similar between May and October. But, the data recorded in Grombalia obtained from the rain gauge station shows that monthly precipitation in October 2020 was 21 mm and as for May 2021, the rainfall recorded was 8 mm. As for annual temperature and rain (2020-2021) 20 • C and 411 mm were recorded. The watershed of Wadi El Bey (475 km 2 ) is located in the northeast and drains the Grombalia, Beni Khaled and Soliman plains. In Fig. 1 , the study area as well as the different sampling points can be observed. Two sampling campaigns were carried out, the first one in October 2020 and the second one in May 2021 period. Thirteen groundwater samples were collected in each campaign, wells GW1-GW13, distributed across the plain at both left and right of the Wadi El Bey (Fig.
(2022). Briefly, HPLC Accelera 600 pump coupled to a Q-Exactive Orbitrap mass spectrometer (Thermo Fischer Scientific, Les Ullis, France) with a HESI source was used for detection and the column for the chromatographic separation was a Waters XBridge BEH C18 (2. 1 × 150 mm and 2. 5 μm particle size). The injection volume was set at 10 μL and the flow rate applied was 0. 3 mL min -1. The separation gradient consisted of water and acetonitrile as mobile phases, both at 0. 1 % of formic acid, starting with 10 % of water from 0 to 1 min, then increasing to a 90 % from 10 to 23 min, and finally 10 % of water from 24 to 29 min. Full scan data were acquired at a resolution of 70,000 and a scan range of 100-1000 m/z. Concerning the MS2, both data-dependent acquisition (DDA) and data-independent acquisitions (DIA) modes were employed at a resolution of 17,500 and a scan range of 50-1000 m/z.
As for the identification parameters, the following were established for confirmation; the parent compound and at least 2 fragments detected within a mass error < 5 ppm, peak intensity higher than 10 4 , as well as an isotopic match of at least 80 %. Once the initial screening was performed, potential candidates were chosen for subsequent DDA-MS2 analysis. The acquired spectra were compared against online available databases such as MassBank, MoNA, or mz cloud for final confirmation, reaching identification level 2a or 2b based on the obtained MS2 spectra  (Schymanski et al. , 2014). For the targeted quantification of the prioritized pesticides, the method used was also developed by  Tadić et al. (2022)  where an Alliance HPLC system (Waters Series 2695) and a reverse-phase column, a Phenomenex Kinetex Polar C18 (100 mm × 4.
6 mm I⋅D × 2. 6 μm particle size) were used. In this case, the mobile phase consisted of Milli-Q and acetonitrile, both with 0. 05 % formic acid, and the flow rate was set at 0. 4 mL min -1. The separation gradient consisted of acetonitrile increasing from 60 to 100 % for 10 min followed by 2 min stabilization and then back to initial conditions. As for the acquisition mode employed, a multiple reaction monitoring mode was used and all analytical data treatment was performed with MassLynx software from Waters where Quantlynx interface was used for the quantification of the potential targeted compounds. Further details concerning both the suspect screening and the target analysis as well as accuracy, precision, linearity, LOQs and LODs of the analytical methods are described in  Tadić et al.
All the lowest PNECs were obtained from the NORMAN Ecotoxicology Database (https://www. norman-network. com/nds/ecotox/). A resulting RQ <1 would relate that the chemical exposure is less than the benchmark considered (PNEC), explaining that the exposure to that certain chemical is unlikely to result in adverse effects to the environment. In order to assess the extent of water pollution in the different investigated sites of the Grombalia Plain, an additive model was used for the calculation of the total risk in any specific site (RQtot). The calculation is made by adding the individual RQ values (RQi) of all the compounds found in a specific sample, following the Eq. (  3 ): RQtot = ∑ n i=1 RQi (3) This model assumes an additive action of the contaminants, without considering the possible synergistic or antagonistic effects  (Barbieri et al.
Targeted CECs To the best of our knowledge, there is a lack of data concerning the occurrence of CECs in groundwater from Tunisia. The list of CECs including commonly prescribed drugs, artificial sweeteners, insecticides, and UV filters was selected mainly based on their prevalence and abundance in the aquatic environment, as well as their widespread human use and consumption. Such a list of CECs was monitored for the first time in groundwater samples from the Grombalia Plain in Tunisia. Sixty-nine compounds, i. e. 59 % of the targeted compounds studied were detected in at least one sample from one campaign (above LOD), covering a broad range of different concentrations. Forty-seven compounds were not detected in any of the samples. Interestingly, commonly found CECs in groundwater, such as the insecticide fipronil and its TPs were not found in any of the samples.
Environmental risk assessment Environmental risk assessment (ERA) was performed for those substances that are ubiquitous in the groundwater samples analyzed and for those compounds which are included in the watch list and are worth considering. Fig. 5  shows the substances evaluated where in this study the approach proposed by  Hernando et al. (2006) , was applied. RQ above 1 is considered as high environmental risk (red), RQ between 0. 1 and 1 medium (yellow) and below 0. 1 as low risk (green). Further details about ERA are reported in the supplementary material (Table  S5 ). The obtained data shows that CAF and ibuprofen pose a high risk to the environment, as well as propoxur at a medium to a high level. In previous studies, caffeine has also been identified as a potential substance causing high risk to algae  (Sadutto et al.
The concentration of oxygenated products was estimated using the sensitivity of 3-hexanone as lower-limit values due to possible fragmentation. All data were analyzed using TOF-Tracer software running on Julia 0. 6 (https://github. com/lukasfischer83/TOF-Tracer) and were further corrected for the duty cycle transmission of TOF and temperature dependent sampling line losses  (Stolzenburg et al. , 2018).
Acknowledgements We thank the  European Organization for Nuclear Research (CERN)  for supporting CLOUD with important technical and financial resources. We thank the  Orbitool team  for developing the tools to analyze mass spectra. This work was financially supported by the  French National program LEFE (Les Enveloppes Fluides et l'Environnement) , the  European Research Council  ( ERC-StG MAARvEL ; no. 852161 ), the  European Union 's  Horizon 2020 research and innovation programme  ( Marie Sklodowska-Curie  grant agreement no. 764991  and  701647 ), the  Swiss National Science Foundation  (no. 200021_169090 ,  200020_172602 ,  20FI20_172622 , and  206021_198140 ), the  US National Science Foundation  ( NSF_AGS_1801280 ,  NSF_AGC_1801574 ,  NSF_AGS_1801897 ,  NSF_AGS_2132089 ), and the  German Federal Ministry of Education and Research  ( CLOUD-16 01LK1601A ).
SSHADE, its solid spectra databases and its future band list of molecular solids Bernard Schmitt 1 , Philippe Bollard 1 , Damien Albert 2 , Lydie Bonal 1 , Olivier Poch 1 , and the SSHADE Consortium * 1 CNRS / Univ. Grenoble Alpes, Institut de Planétologie et Astrophysique de Grenoble, Grenoble Cedex 9, France (bernard. schmitt@univ-grenoble-alpes. fr) 2 CNRS / Univ. Grenoble Alpes, Observatoire des Sciences de l'Univers de Grenoble, Grenoble Cedex 9, France * A full list of authors appears at the end of the abstract The SSHADE database infrastructure (http://www. sshade. eu) hosts spectral data of many different types of solids: ices, snows, minerals, carbonaceous matters, meteorites, IDPs and other cosmomaterials,… covering a wide range of wavelengths: from X-rays to millimeter wavelengths.
Its Search / Visualization / Export interface is open to the community since February 2018. It currently contains over 3500 spectra. We are currently developping a 'band list' database that will provide the band parameters (position, width, intensity, isotopic species, attribution mode,. ) of a number of solids of astrophysical and planetary interest in various phases (crystalline, amorphous,. ) and mixtures (different compositions) and at different temperatures or pressures. We will first feed this database from critical compilations of all data published in various journals for pure ices and molecular solids (hydrates, clathrates, sulfur compounds,. ) and their mixture, including the own works of the members of the SSHADE consortium. An efficient search tool will allow to filter on various parameters such as band position (or spectral range) and intensity, expected molecular or atomic formula, type of vibration (e.
g. infrared, Raman, fluorescence. ) and display the results graphically. A demonstrator prototype is online in the 'old GhoSST' database (https://ghosst. osug. fr/search/band). This band list will be a key tool for astronomers and space explorers to identify unknown absorption bands observed in the spectra of the surface or atmosphere of many objects in the solar system. Once the best candidate solid selected by the user the tool will point to the best relevant spectral data present in the SSHADE databases.
Briefly, all nutrients taken up and secreted in milk and blood CO 2 were expressed in millimoles per hour of carbon. The MGU of TG was used to estimate the MGU of glycerol taken up because the analysis measured total glycerol. Fatty acid yields for carbon balance were calculated as described in  Lemosquet et al. (2009b)  using fat yield of the left half-udder during the d 13 evening milking and FA composition of this milk (Table  4  and Supplemental Table  S1 ; https: / / doi. org/ 10. 5281/ zenodo. 8366232;  Anger et al. , 2023) , based on the hypotheses that (1) all the FA in milk are TG  (Palmquist et al. , 1969)  and (2) each TG has the following composition: TG + 3H 2 O = 3 × FA + glycerol. Then FA (mol/12 h) = FA % fat (g/12 h) MW + 1 3 MW FA glycerol ( )× × --MW H O 2 , where MW represented molecular weight of FA, glycerol, and H 2 O, respectively.
Statistical Analyses Intake, milk yield, and milk composition of the last 5 d of each period were averaged as described in the "Calculations" section and used for statistical analyses. Daily means of the 6 blood samples collected on d 13 were used for mammary metabolism and associated calculations. Data were analyzed using the MIXED procedure of SAS v. 9. 4 (SAS Institute Inc. , Cary, NC) according to the following statistical model: Y ijkl = µ + cow i + period j + energy k + protein l + (energy × protein) kl + ε ijkl , where µ is the mean; Y ijkl is the dependent variable for the fixed effects of period j (4 periods), energy k is the energy supply (LE or HE), protein l is the protein supply (LP or HP), cow i is a random effect, and ε ijkl is the residual error associated with observations ijkl.
Because the Latin square was balanced for residual effects, these effects were tested and found not significant (P > 0. 20) for any of the 4 treatments, which indicates that there was no carryover effect. The results were assessed as least squares means ± the largest standard error. The homogeneity of residues was verified. Differences were considered significant at P ≤ 0. 05 and trends for 0. 05 < P ≤ 0. 10. If the energy × protein interaction was significant or tended to be significant, the SLICE procedure of SAS was used to test the effect of one factor within the other factor.
09) in the mammary clearance rate of TG: increasing the MP supply increased it with the HE treatment (P = 0. 02 from the SLICE test). Blood O 2 clearance rate was higher in the HE treatment compared with the LE treatment (+49%, P = 0. 02). Mammary arteriovenous differences are presented in Supplemental Table  S2  (https: / / doi. org/ 10. 5281/ zenodo. 8366232;  Anger et al. , 2023). Mammary Net Uptake. Mammary plasma and blood flows (Table  7 ) were higher with the HE treatment than the LE treatment (+32% and +31%, respectively; P < 0. 01). Glucose MGU (Table  7 ) was higher with the HE treatment than the LE treatment (+30%, P < 0. 01) and tended to be higher with the HP treatment than the LP treatment (+16%, P = 0. 07). Lactate MGU did not differ significantly from zero with any treatment (t-test).
ACKNOWLEDGMENTS We thank  Jean-Louis Peyraud , former director of the former  INRA Mixed Research Unit Production du Lait (Saint-Gilles, France ), which funded this experiment, and  Cargill (Crevin, France)  for the  CIFRE (Conven-tion Industrielle de Formation par la Recherche)  PhD grant of J. C. A. The authors acknowledge  H. Rulquin  ( INRA, Saint-Gilles, France ) for help in preparing the experiment;  F. Bardey  ( INRA ) for initial data analyses;  E. Pascal  ( Clinique Veterinaire des Faluns, Saint Gregoire, France ) and  R. Delagarde  ( INRA, Saint-Gilles, France ) for animal surgeries;  D. Chevrel ,  A. Cozien , and  J. L. Harel  ( INRA, Installation Expérimentale de Production Laitiere, Le Rheu, France ) for animal management; and  N. Huchet ,  M. Texier ,  S. Marion , and  T. Le Mouel  ( INRA, Saint-Gilles, France ) for their help with laboratory analyses.
, 2017; Röös et al. , 2017; Theurl et al. , 2020; Wiebe et al. , 2015) , as well as historical developments of fossil fuel related GHG emissions  (Andres et al. , 1999; Andrew and Peters, 2021; Boden et al. , 2017) , relatively little is known about long-term historical trends of agricultural GHG emissions, especially on sub-global scale. Research available for the period since 1960 shows that increased agricultural emissions went along with declining emissions per unit of agricultural product globally, with strong variations across regions  (Bennetzen et al. , 2016; Hong et al. , 2021). However, the period before 1960 has only rarely been investigated. Garnier et al. (2019)  presented a comprehensive dataset on all agricultural GHG emissions for France from 1852 to 2014, and  Parton et al.