dataesr/scientific-paragraphs-categorization huggingface dataset:
Size and Relative Abundance Data We measured individual foraminiferal size based on the test maximum diameter. The test maximum diameter was calculated from the area using the formula: MD = ( √ Area ( mm 2 ) 𝜋𝜋 ) * 2. We chose to measure test size using the maximum diameter because it is less affected by random object orientation  (Schmidt, Renaud, et al. , 2004)  and has been reported to be a consistent size proxy for two-dimensional images  (Brombacher et al. , 2018). The 95th percentile size of each assemblage, hereafter referred to as size 95/5 , was selected to investigate the size response of planktonic foraminifera assemblages to environmental variables. We chose size 95/5 because, compared to mean and maximum size, it is less sensitive to single outliers  (Rillo et al. , 2020; Schmidt, Renaud, et al.
, 2004). To validate our size spectra, we compared our size 95/5 data with published measurements reported for the TIO from core-top samples  (Rillo et al. , 2019 (Rillo et al. , , 2020)). We also compared the relative abundances of the species recorded in this study with those reported for the closest sites to our core locations in the ForCenS Database, which compiles planktonic foraminifera assemblages from core-top sediments  (Siccha & Kucera, 2017).
Statistical Analyses Before analyzing the planktonic foraminiferal size and assemblage data, we excluded rare species (i. e. , species with <50 individuals per sample in all samples). We also excluded samples with low whole specimen counts (<300 whole planktonic foraminifera in a sample); this brought the total number of core-top samples analyzed from 82 to 62. We used the Jarque-Bera normality test  (Jarque, 2011)  in the R package "tseries" (version 0. 10-51) to evaluate whether our size data set showed a normal distribution (based on skewness and kurtosis). This enabled us to decide whether parametric or non-parametric correlation and regression analyses would be preferable to explore relationships between variables. Results of the Jarque-Bera normality test confirmed that the distribution of our size data set is non-parametric (X-squared = 478,591, p < 2.
2e-16); therefore, only non-parametric correlations and regression analyses were adopted going forward. For comparison purposes, we restricted the  Rillo et al. (2020)  data set to the TIO (30°S-30°N, 54-112°E) and utilized shell diameter data instead of shell area. Using the Spearman's rank-order correlation (""), we compared our size 95/5 data with that contained in the  Rillo et al. (2019)  data set. Likewise, we compared the species' relative abundances recorded in this study with those reported for the closest sites to our core locations in the ForCenS Database (REF). Aside from making comparisons based on size measurements, through robust regression analysis, we also assessed the relationship between the size 95/5 of nine planktonic foraminifera species (Pulleniatina obliquiloculata, Globorotalia menardii, Neogloboquadrina dutertei, Globigerinoides ruber albus, Trilobatus sacculifer, Globigerinella siphonifera, Globigerinoides conglobatus, Globorotalia truncatulinoides, and Globoconella inflata) and mean annual SST as contained in the  Rillo et al.
(2019)  data set for the TIO. We used the "robustbase" package (version 0. 95-0) in R for this and subsequent robust regression analyses. A size frequency distribution (SFD) was used to show the size ranges, the number of observations within a given size, as well as the number of species represented across the study sites. We determined the modal distribution of each species' SFD using the "LaplacesDemon" package (version 16. 1. 6) in R which tests for unimodality, bimodality, and multimodality in a given distribution. Through robust regression analyses, we tested the OSH by: (a) examining the relationship between individual species' size 95/5 and their relative abundance, assuming that relative abundance is a measure of optimum growth conditions, and (b) investigating the relationship between individual species' size 95/5 and environmental parameters.
We further explored the relationship between size 95/5 , species richness (defined as the number of species in each sample), and species diversity through robust regression analyses. Species diversity was calculated using the classical Shannon-Weiner diversity index  (Shannon & Weaver, 1949) : 𝐻𝐻 = 𝑠𝑠 ∑ 𝑖𝑖=1 𝑃𝑃 𝑖𝑖(ln 𝑝𝑝𝑖𝑖) where, H = Shannon-Weiner diversity index S = Total number of species encountered Pi = proportion of ith species in the population ln = natural logarithm ∑ = sum from species 1 to species S We assessed the influence of assemblage composition on size 95/5 in two ways: first, in each sample, we investigated the size spectrum of the 13 most abundant species relative to the sample size 95/5 using the density function in the R ggplot2 (version 3. 3. 6) package  (Wickham, 2016) ; second, through robust linear regression analysis, we evaluated the relationship between size 95/5 , the total number of foraminifera, and the percentage contribution of species with sizes greater than the regional size 95/5 in our data set.
Relative Abundance Comparison With ForCenS Database Comparison of the relative abundance of the 21 most abundant species from our study sites with the corresponding closest sites (pairing was based on a single site) in the ForCenS Database  (Siccha & Kucera, 2017)  shows a significant correlation (Figure  4a ). While the data fall near the y = x line and the correlation between both data sets is strong (ρ = 0. 77, p < 2. 2e-16), some differences in the relative abundances of some species occur. These could result from differences in the total number of individuals counted in this study (2,000 individuals per sample on average) compared to that of the ForCenS database (300 individuals per sample on average). Nevertheless, the similarity between the two data sets becomes more evident when we observe the regions of highest relative abundance of some of the species.
Due to misclassification of species with low abundance, the CNN classifier underestimates the population of minor species, but this bias is noticed by the human classifier. Similar to human classifiers, the CNN classifier accuracy improves as the average species count increases. The strong correlation (ρ = 0. 97, p < 2. 2e-16) between the number of individuals counted by the CNN versus human classifiers across 21 species highlights the reliability of the data produced using our "Base Cyclic 16" neural network (Figure  2b ). This is further supported by our results correlating the relative abundance data generated by the model with the data retrieved from the closest stations to the cores used in this study from the modern surface sediment in the ForCenS database (Figure  4a ); this also showed significant positive correlation (ρ = 0.
77, p < 2. 2e-16). Species such as G. ruber albus, G. glutinata, and G. inflata, recorded the highest relative abundance in the ForCenS database at similar locations in our study (Figure  3 ). Although skewed toward large sizes, the relatively comparable size spectra of the  Rillo et al. (2019)  and the data set produced by MiSo provides confidence in the use of automated workflows such as MiSo/ParticleTrieur in the identification, classification, and measurement of planktonic foraminiferal species (Figure  4b )  (Marchant et al. , 2020). Our workflow and CNN model show a better accuracy (89. 9%, Figure  3a ) than other workflows utilizing supervised learning models in the automatic identification of planktonic foraminifera, for example, the "VGG 16" model test (27,737 training images, 6,903 validation data set) by  Hsiang et al.
In our full data set, we find that higher relative abundances of small-sized upwelling species occur under higher surface carbonate ion concentration and that higher relative abundances of large-sized oligotrophic species occur under lower surface carbonate condition  (Figures S6a and S6b in Supporting Information S1). This leads to an overall lower assemblage size 95/5 under high surface carbonate condition and a higher assemblage size 95/5 under low surface carbonate ion conditions. However, this result is sensitive to the inclusion of the Arabian Sea data set, and its exclusion leads to opposite results (i. e. , high relative abundances of small-sized species under low surface carbonate ion conditions and vice versa) (Figures S6c-S6d and S7 in Supporting Information S1). Overall, in the TIO, our results show that surface carbonate ion concentration exerts primary control on assemblage size 95/5 variations, and we suggest that this response might be due to energetic optimization and/ or changes in the dominance of the main species linked to nutrient regimes, though this pattern remains to be confirmed in other ocean basins.
Acknowledgments We acknowledge the  Flotte Océanographique Française  for recovery and access to all  Marion-Dufresne  samples. The authors thank  Jean Charles Mazur  for laboratory assistance. This project is funded by an  AMU   President's Scholarship , the  CNRS-INSU LEFE IndSO  Grant,  ANR Grants FIRST  ( ANR-15-CE4-0006-01 ) and iMonsoon ( ANR-16-CE01-0004-01 ).
Data Availability Statement Data sets associated with this paper are available in the SEANOE open-access database at https://doi. org/10. 17882/86211 (Adebayo, Bolton, Marchant, Bassinot, Sandrine, et al. , 2022)  and https://doi. org/10. 17882/86411 (Adebayo, Bolton, Marchant, Bassinot, Conrod, et al. , 2022) , published under a CC-BY-NC license. The scripts for the code used in this work can be accessed at https://github. com/mikolinton. The Parti-cleTrieur software used in this work is available with a tutorial from http://particle-classification. readthedocs. io, and the Python scripts to train the CNNs in this paper are available from http://www. github. com/microfossil/ particle-classification and can be installed using the pip Python package.
2°C (www. ifremer. fr/cerweb/ deboyer/mld)/Mignot et al. (2007) Pycnocline depth/2°m Values obtained for each core location pycdepth NOAA/(https://data. noaa. gov/ dataset/dataset/mixed-layer- depth-isothermal-layer-depth- barrier-layer-depth-and-other- upper-ocean-thermohaline) Mixed layer depth/1°m Surface mixed layer depth Mld_ifremer IFREMER/LOS Mixed Layer Depth Climatology website (www. ifremer. fr/cerweb/ deboyer/mld/surface_mixed_ layer_depth)/de Boyer Montégut et al. (2004) Mixed layer depth/1°m Surface mixed layer depth Mld_NOAA NOAA/(https://data. noaa. gov/ dataset/dataset/mixed-layer- depth-isothermal-layer-depth- barrier-layer-depth-and-other- upper-ocean-thermohaline) Core depth/m m Values retrieved from cruise reports Depth Cruise Reports 15252027, 2023, 4, Downloaded from https://agupubs.
2003). They concluded that extracting 1. 2 BCM/year would cause a drawdown greater than 140 m after 100 years and would lead to a cone of depression that could affect other reclaimed areas in El-Kharga and Dakhla Oases  (Ebraheem et al. 2003). Also, El-Alfy (  2014 ) developed a MODFLOW model to test an extraction rate of 1. 4 BCM/ year and found it would cause a drawdown of 30 m at the end of a simulation period of 30 years (1 m/year). It is worth mentioning that the area under cultivation currently (2022) in East Oweinat is 378,563 ha, making these studies quite contradictory with regard to their conclusions. A summary of methodologies used in modelling studies for the NSAS in the Western Desert during the last 20 years is given in Table  1. This compares the numerical code used, areas modeled, assumed boundary conditions, input hydraulic parameters, data used for calibration, and the expected drawdown due to the management scenarios considered.
88 MCM/day from 1,000 wells, which would cause a drawdown of 92 m (less than 1 m/year). Ragab et al. (2019)  used the Visual MODFLOW software to test different pumping scenarios over a 50-year period. They predicted a drawdown of 369 m for an abstraction of 1. 86 Mm 3 /year (full development of 170,000 feddan) and recommended reducing planned pumping rates by 70% to extend the lifetime of the aquifer. It is believed that the aquifer is "probably recharged through upward leakage under artesian conditions"  (Sayed et al. 2020) , which serves to underline the high uncertainty inherent in these quantitative projections. Wadi El-Natroun is a depression to the east of El-Moghra Aquifer (Fig. 2b ) with rapid and unregulated groundwater exploitation during the past two decades. Ahmed et al. (2015)  used MODFLOW to simulate the current extraction rate (56,428 m 3 /day) in this restricted area and predicted a drawdown of 3.
Most of the studies assumed closed boundaries for their study area, i. e. , the impact of excessive abstraction from one location to other adjacent areas in the same aquifer system and the interaction between aquifer systems on a regional scale were not examined. For the NSAS, most of the reviewed studies used very limited data (e. g. ,  Gad et al. 2011; Moharram et al. 2012) , and only a few of them used historical data, limited however to a few wells in the study area, to calibrate their models (e. g. , El-Sheikh 2015;  Sharaky et al. 2019). The same applies to the El-Moghra Aquifer-the data used for calibration were limited, some studies using old contour maps  (1988 and 1991)  from the literature (e. g. ,  Youssef et al. 2012; Ragab et al. 2019) , while others used instant measurements of water levels in a limited number of wells which were insufficient to represent the whole aquifer system.
The planned reclaimed areas for the first phase of the mega reclamation project (4 million feddan) include 876,000 feddan (367,920 ha) in the Western Desert, i. e. ~4. 38 BCM of groundwater, considering an average requirement of 5,000 m 3 /feddan. Total groundwater extraction from the NSAS was estimated in 1998 at 0. 95 BCM/year (USAID 1998). Since 1998, data about the total groundwater abstraction from the NSAS at a regional scale have not been shared and are mostly underestimated. Abdel-Shafy and Kamel (2016) estimated the extraction from the NSAS in the Western Desert at 1. 65 BCM/year. The Ministry of Water Resources and Irrigation (MWRI 2005) estimated the total amount of deep groundwater extraction at 2. 1 BCM in 2015 and was planning to double this amount to reach 4 BCM by 2037 (MWRI 2017).
5 MCM/day, is currently under construction. The aim is to develop a 'New Delta' reclamation megaproject, announced in 2022, which could contribute to the recharging of El-Moghra Aquifer (Fig. 3b ). It is noted that such a rapid constraint was anticipated by  MWRI (2004) , which expected salinity levels of extracted groundwater to increase threefold by 2030; however, this did not deter expansion in the area. Thus, sustainability of nonrenewable groundwater must also include water quality issues, as well as social and economic consequences in the short and long-term horizons (World Bank 2003). In the early days, during the period from 1960 to the middle of 1995, water levels, piezometric heads and groundwater salinity data were collected every 3 months by the General Authority for Rehabilitation Projects and Agricultural Authority (GARPAD) and the New Valley Development Authority (NVDA).
Since then, the Ministry of Water Resources and Irrigation (MWRI) took over the responsibility of groundwater monitoring through the Research Institute of Groundwater (RIGW). RIGW established the National Groundwater Quality Monitoring Network (NGWQMN) in 1998 to collect water samples for hydro-chemical analysis, in addition to instant measurements of water levels, on a yearly basis from production and observation wells at depths ranging from 1,200 to 50 m, mostly located in the Nile Delta and Valley area, in addition to a few deep wells located in the Western Desert. Recently, the MWRI imposed the construction of one observation well every 420 ha at the expense of the private sector under the supervision of the Groundwater Sector of MWRI, which also has its own data. Currently, datasets are not shared and are difficult to access, because the government considers them as national security information.
Acknowledgements The authors would like to thank the  Science and Technology Development Fund (STDF-Egypt)  for the financial support received under the framework of a project (ID# 46278 ). Funding Open access funding provided by The  Science, Technology & Innovation Funding Authority (STDF)  in cooperation with The  Egyptian Knowledge Bank  (EKB). This paper is a part of a project titled " Optimal Exploitation Strategies for Sustainable Utilization of Fossil Groundwater Reserves in Egypt " funded by the  Science and Technology Development Fund (STDF), Egypt.
Second, comparing our dataset to midday water potentials (Ψ min ) monitored in vineyards during rainfed experiments indicated that grapevine varieties barely reach water potentials associated with critical thresholds of xylem embolism in perennial organs. This is particularly the case for the commonly studied Vitis vinifera cv. Cabernet Sauvignon, for which reported Ψ min in vineyards were not lower than -1. 6 MPa  28, 53, 54  whereas the onset of embolism (c. f. Ψ 12 ) in stem was found at -2. 2 MPa. Other instances include Vitis vinifera cv. Merlot, whose stem Ψ 12 of -1. 8 MPa is lower than Ψ min of -1. 5 MPa measured in Israel across the 2011-2012 seasons  55  , and Vitis vinifera cv. Tempranillo, whose stem Ψ 12 = -1. 9 MPa is lower than Ψ min of -1. 4 MPa monitored in Spain across the 2000-2004 seasons  56.
To examine this, the varietal embolism vulnerability dataset was coupled with a database of regional and national winegrape bearing areas by variety  72  (Supplementary Data S1, Supplementary Fig. S6a, b  for examples on France and New Zealand). Here, the regional risk of drought vulnerability was voluntarily examined under a no-irrigation (dryfarmed) scenario, because (i) the majority of vineyards in Europe are rainfed, and (i) irrigation, that becomes increasingly necessary in hotter regions worldwide, will simply increases competition for fresh-water resources, making the search for varieties better adapted to drier climate all the more relevant. We calculated the bearing area of each embolism vulnerability cluster per wine region and country (Supplementary Fig. S6c, d  for examples on France and New Zealand), and estimated a risk index of large-scale drought vulnerability for each region for which the varietal coverage of our dataset represented at least 40% of the regional or national winegrape bearing area (Supplementary Data S2).
(proles) pontica) and four geographical groups (two varieties from Balkans, three from Eastern Mediteranean and Caucasus, two from the Iberian Peninsula, and 15 from Western and Central Europe)  76, 77. Accounting for ~ 1,9 M hectares cultivated, they represented > 42% of the global winegrape bearing area (data from https:// www. adela ide. edu. au/ press/ titles/ wineg rapes). The study included in particular nine of the top 10 winegrape varieties, namely Cabernet Sauvignon, Merlot, Tempranillo, Chardonnay, Syrah, Grenache, Sauvignon blanc, Ugni blanc and Pinot noir.
The main stems were then kept in water for relaxation during ca. 1 h prior to measurements, when they were cut under water to a standard length of 1 m. A solution of ultrapure and deionized water containing 10 mM KCl and 1 mM CaCl 2 was used as reference ionic solution, while the centrifuge rotation speed was initially set to induce a xylem pressure of -0. 8 MPa before being gradually increased to lower xylem pressures by -0. 5 MPa. Hydraulic conductivities at every rotation speed (k i , kg m -1 s -1 MPa -1 ) were measured using the Cavisoft software (v. 5. 2, University of Bordeaux). The percentage loss of hydraulic conductivity (PLC) was determined at each pressure as follows: where k max represents the maximum hydraulic conductivity measured at the first induced xylem pressure (i. e. -0.
They were fixed with a transparent glass and adhesive tape to avoid as much leaf shrinkage as possible during plant desiccation. Scan magnification was set with the Epson scanner software to give sufficient resolution of midrib and other order veins. The following settings were selected: film (with film area guide) as document type, color positive film as document source, 8-bit grayscale as image type, and a 2400 dpi resolution. Brightness and contrasts as well as leaf scanned area were adjusted to optimize visualization of embolism events and provide images not exceeding 9 Mb each. Each leaf was automatically scanned every five minutes using the AutoIt automation software until mesophyll cells turned from green to brown, which indicated cell death. Complete plant dehydration in spring and summer was observed after ca.
S7b ). Upon completion, the stacks of captured images, which comprised between 1400 and 2200 scans, were analyzed using ImageJ software  78  in order to reveal embolism events (i. e. air entry into leaf xylem vessels), that corresponded to rapid changes in light transmission through leaf veins (see https:// www. opens ource ov. org/ for a detailed description of the analysis process). Briefly, an image subtraction method was used to emphasize changes in light transmission between successive images of a given stack of images. The new stack of subtracted images was then thresholded to reveal embolism events, appearing as white pixels on a dark background, before the ' Analyze Particles' function was used to filter out noise, corresponding to small changes small random changes in pixel contrast compared to large, structured embolism events  35  , and automatically count the number of embolized pixels observed in leaf veins.
All embolized pixels recorded throughout plant dehydration were then summed up to determine a time-based accumulation of embolism. These data were then combined with the water potential timeline to determine stem water potential associated with each embolism event. An optical vulnerability curve was determined by representing the accumulation of embolized pixels as a function of Ψ leaf (see the following section). Vulnerability curve fitting. Vulnerability curves, corresponding to percentage loss of hydraulic conductivity (PLC, %) as a function of xylem pressure (for the centrifugation technique) or percentage of embolized pixels (PEP, %) as a function of stem water potential (for the optical vulnerability technique), were fitted using the NLIN procedure in SAS 9. 4 (SAS, Cary, NC, USA) based on the following equation  79  : where Ψ 50 (MPa) is the xylem pressure/water potential inducing 50% loss of hydraulic conductivity and S (% MPa -1 ) is the slope of the vulnerability curve at the inflexion point.
Stem pieces were sampled in the middle of the 1-m centrifuged branches (n = 50), and placed in 80% ethyl-alcohol. Stem cross-sections (50 µm thick, bark removed) were made using a GSL1 microtome  80  , stained using a 1% safranin O solution (96% ethyl-alcohol), rinsed twice in 100% ethyl-alcohol and transferred in xylene. Slices were then mounted between slide and coverslip in resin (Histolaque LMR, Labo-Moderne, Gennevilliers, France). High resolution micrographs (~ 500 nm/pixel) were obtained at the Bordeaux Imaging Center (a member of the France Bio-Imaging national infrastructure, ANR-10-INBS-04) using a NANOZOOMER 2. 0HT (Hamamatsu Photonics, Hamamatsu City, Japan) in brightfield mode. Xylem vessel areas were measured in the total area of each cross section using ImageJ software  78. The following anatomical traits were calculated following Scholz et al.