dataesr/scientific-paragraphs-categorization huggingface dataset:
ET a was derived from latent heat (λE) flux analysis with the eddy covariance technique (Li-7500A (LiCor) + Windmaster Pro (Gill)), using Tourbillon (INRAE) software 20 Hz raw data acquisition and post-processing of binary files using EdiRe (University of Edinburgh, Scotland) as follows: (1) spike removal in anemometer or gas analyser data; (2) unit modifications and statistical operations; (3) planar fit; (4) removing time lag from H 2 O and CO 2 through a cross-correlation procedure; (5) computing mean values, turbulent fluxes and relevant parameters, e. g. the Monin-Obhukov stability index (Z/L); (6) high-frequency corrections via transfer functions based on Kaimal-Moore's co-spectral models  (Kaimal et al. 1972 , Moore 1986) ; and (7) performing a correction to account for the effects of fluctuations of temperature and water vapour on measured fluctuations in CO 2 and H 2 O  (Webb et al.
1980). According to data quality control protocols, incorrect data was removed to keep only reliable flux measurements, according to  Foken and Wichura (1996). Gap-filling of λE was performed using the ReddyProc package  (Wutzler et al. 2018). Meteorological data was provided by an automatic station (CR1000, Campbell Sci. ) equipped with classical sensors. Measured parameters included air temperature and relative humidity (CS215, 2 m), wind speed (WindMaster sonic anemometer (Gill Instruments, Lymington, UK), 4. 5 m and estimation at 2 m) and rainfall (TE25MM). The volumetric SWC was measured at depths of  15, 30, 50, 75, 100, 125, 150, 175, and 200  cm, out of the influence of the trees  (Roupsard et al. 2020). Data was recorded using automated time domain reflectometers (type CS615 Campbell) which were installed horizontally from a soil pit in undisturbed soil, and the soil pit was refilled after installation.
The sensors were calibrated from in situ volumetric water content derived from gravimetric water content and bulk density measurements during both dry and wet seasons (Supplementary material, Fig. S1 ). The normalized difference vegetation index (NDVI) was monitored semi-hourly using a calibrated sensor described in detail by  Pontailler et al. (2003) , located at 20 m height and pointing downwards to the crop area next to the TDR pit. NDVI data were processed and filtered according to  Soudani et al. (2012). Millet leaf area index (LAI) was measured from millet leaf biomass and specific leaf area in 12-18 quadrants (15 m 2 each) at the time of harvest according to  Roupsard et al. (2020)  and used to convert the time series of NDVI into crop LAI. In millet, we observed that senescent leaves hardly detach from the stalk (sheath) and that only a moderate fraction of leaves (20-30%) are brown at harvest: the whole biomass of green and brown leaves was weighed at harvest, and we assumed that the slow decay of NDVI between the time of max NDVI and harvest was due mostly to moderate leaf senescence (not litterfall), so that total LAI was quite realistically estimated.
5 on average for many soils  (Mualem 1976 ). The Feddes model  (Feddes et al. 1978 ) was used to compute the root water uptake of the crops: S z; t ð Þ ¼ ψ h; z ð Þ � β z ð Þ � T p (6) ψ h; z ð Þ ¼ 1 if h1 � h < 0 hÀ h2 h2À h1 if h2 � h < h1 0 if h1 � h2 8 < : (7) where S(z,t) is the root uptake; Ψ(h,z) is the response function of water stress; T p is the potential transpiration; and β(h,z) is the standardized distribution function of root water uptake, which describes the spatial distribution of root water uptake; h1 and h2 are the soil water potential with root water uptake rates of 1 and 0, respectively. The following parameters were used for the Feddes model: h 1 = -15, h 2 = -30, h 3high = -325, h 3low = -600, and h 4 = -8000 cm; they were adapted from HYDRUS-1D database for corn  (Wesseling 1991)  as no values were available for groundnut or millet.
A nonlinear Marquardt-Levenberg parameter optimization approach with an objective function described in detail by  Šimůnek et al. (1998)  was used to optimize θ s , α, K s and n within a range of minimum and maximum values: θ s (0. 25-0. 5 cm 3 cm -3 ), α (unconstrained), K s (1-15 cm h -1 ), and n (1. 2-3). The parameters obtained from the second approach were used as an initial estimate for the inverse procedure with one (M3a) or three soil layers (M3b). For each method, the pore connectivity (l) was assumed to be 0. 5  (Mualem 1976 ). Regarding the residual water content, the value proposed by most PTFs was greater than the initial SWC from TDR sensors. Therefore, θ r was set to 0 for all methods. Four-year datasets (from May 2018 to December 2021) were used to compare the measured and simulated SWC and ET a from each method.
Inverse parameter estimation in M3a and M3b was performed using the data from 9 May 2018 to 1 March 2020 (662 days) for calibration, and the rest of the evaluation period was for validation (617 days). Statistical indicators were used to evaluate HYDRUS-1D predictions relative to measured SWC and ET a data from the field. The indicators used are the coefficient of determination (R 2 ), the root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE). These statistical parameters were further analysed using Taylor plots  (Taylor 2001 ). The Taylor plot provides a way of showing how these complementary model performance statistics vary simultaneously as they are related to one another. The similarity between simulated SWC and ET a observations is quantified by their correlation R 2 (azimuthal position), root mean square difference (concentric dashed line emanating from the "observed" point), and the amplitude of their variations, represented by their standard deviations (radial distance from the origin).
These two latter methods were the most accurate PTFs methods in terms of standard deviation, R 2 and RMSE values. Regarding the inverse parameter estimation, both methods agreed well with observed data at all Table  2. Soil hydraulic parameters (SHPs) assessed by three approaches: class pedotransfer function (PTF) using loamy sand texture (M1a to M1e), neural network PTFs from Rosetta package using the percentage of silt, sand and clay and bulk density data of topsoil samples (M2a) and three-layer soil description (M2b), and inverse parameter estimation of soil moisture data using a single soil layer (M3a) and three-layer soil (M3b). Values in parentheses are the standard deviation associated with the inverse parameter estimation. θ s is the saturated water content, θ r is the residual water content, α is the inverse air entry value, K s is the saturated hydraulic conductivity and l is the tortuosity.
2019). For instance, most PTFs developed in the USA or Europe were not suitable for properly simulating the vadose zone processes for Sahelian soil under semi-arid climate conditions. Furthermore, PTFs might report SHPs for the MvG model combinations, which may disagree with physical soil properties. For example,  Lehmann et al. (2020)  evidenced non-physical values of α and n in global SHP databases based on the characteristic length (L C ) for capillary flow that rarely exceeds 1 m. L C is normally short in coarse (narrow pore size distribution) and fine textures (high flow resistance) but large in intermediate textures  (Lehmann et al. 2008). Based on Rosetta PTFs,  Lehmann et al. (2020)  computed the corresponding L C that exceeds several metres for 30% of the global terrestrial area, thus considered non-physical.
Indeed, HYDRUS-1D uses Beer's law for ET 0 separation, which will be forced as upper boundary conditions (potential transpiration and evaporation). Thus, ET a partitioning was more driven by the surface cover fraction, specifically the LAI and the extinction coefficient by the canopy. Overall, our LAI data temper the results of  Velluet et al. (2014)  for millet in terms of intensity (LAImax in Niger around 0. 5 m 2 m -2 ; same result obtained here) for, however, a longer growing season (three months longer in Niger: from July to January) resulting in a T a of 143 mm year -1 (30. 8% of P). They also showed that evaporation was the most significant water flux component, estimated on average at 240 mm year -1 (51. 6%), which is in line with our findings (for e. g. 256-324 mm year -1 , i. e. 56.
Conclusions Soil water balance modelling for a large area such as the Sahel deserves both highly instrumented site experiments and simple but robust indicators for upscaling when few data are available. HYDRUS-1D was used with nine parameterization methods based on class PTFs, neural network PTFs and inverse calibration of SWC to simulate water fluxes in Sahelian loamy sand soil with a four-year dataset under crop rotation of pearl millet and groundnut. The model predictions were compared with field measurements from soil moisture sensors installed at nine depths (from 15 to 200 cm) and ET a from eddy covariance fluxes. Class PTFs resulted in the least accurate approach for simulating SWC and ET a , with low model performance for Woesten, Weynants, and Tóth classes and moderate performance for Carsel&Parrish and Rosetta classes.
Acknowledgements D. M. L. Diongue  received a scholarship from  CIRAD  and is grateful to the  SF-Team research group  from  UCAD. Faidherbia-Flux  received funding from  EU-LEAP-Agri  ( RAMSES II ),  EU-DESIRA (CASSECS) ,  EU-H2020  (SustainSahel), AGROPOLIS and TOTAL Foundations (DSCATT),  CGIAR (GLDC). O. Roupsard  is indebted to  P. Berbigier ,  J. M. Bonnefond  and  M. Irvine  ( INRAE Bordeaux ) for long-term support on EC. We are highly grateful to the people of  Sob-Niakhar , in particular  I. Diouf ,  A. Diouf  and  R. Diatte. We also thank the two reviewers for helping to improve the clarity and applicability of this manuscript.
Data collection Data for the meta-analysis were extracted from literature published between 1980 and January 2021, collected from Web of Science, Google Scholar, and Scopus database (Details of the keywords used are shown in Online Resource 1). We selected only studies that spectrally selectively attenuated solar radiation to measure the photodegradation of surface leaf litter in terrestrial ecosystems. We excluded any studies that did not separate single wavebands or that did not allow the effect of single wavebands to be calculated due to the lack of a control treatment. Since one of our aims was to understand the effects of spectral composition on mass loss under ambient sunlight, all studies employing supplemental radiation were excluded. Moreover, as we aimed to examine the correlation between photodegradation rate and litter traits, we retained only studies employing leaf litter from a single species, while we excluded studies using litter mixtures (More details about study selection are found Online Resource 1).
We considered dark treatments to be only treatments blocking more than 95% of the solar spectrum. We extracted data concerning litter mass loss and initial litter traits. Where data were not presented in tables, we extracted them directly from the gures using WebPlotDigitizer 4. 2  (Rohatgi, 2019). We retained a total of 30 articles (The list of retained studies is shown in Online Resource 2) which produced a total of 325 datapoints. Several papers included comparisons of multiple plant species (A list of the 114 species included is shown in Online Resource 3), eld sites and spectral treatments. The effects of ve spectral regions were calculated: 1) UV radiation (280-400 nm); 2) UV-B radiation (280-315 nm); 3) UV-A radiation (315-400 nm); 4) blue light (400-490 nm) and 5) the full spectrum of visible light and UV radiation.
The effect of each spectral region was obtained by comparison of pairs of spectral treatments applied in the original studies: the effects of excluding UV radiation, UV-B radiation and the full-spectrum were obtained by comparison of the control treatment with the no-UV, no-UVB and dark respectively; while the effect of UV-A radiation was obtained by comparison between the no-UV and the no-UVB treatment and the effect of blue light by contrasting the no-UV/blue and no-UV treatments as in (Wang et al. , 2020). There were too few studies to be able to test the effects of green light. Additionally, we extracted complementary information from each study: ecosystem (grassland, shrubland, woodland and open area); length of the decay period (the duration of the experiment in months); habit (evergreen or deciduous); litter form (herbaceous; shrub, tree); latitude (details about the categorisation of these data and complementary information are shown in Online Resource 4 and in the dataset  (Pieristè et al.
, 2021). The climate at each study site was de ned according to the updated Koppen-Geiger climate classi cation using the map provided by  (Beck et al. , 2018) , which divides the globe into ve major climatic zones further separated into subdivisions based on temperature and precipitation (Details of the climate classi cation are shown in Online Resource 5). In order to deliver estimate global-scale quantities of C released from surface litter by photodegradation, we extracted data from the Soil Respiration Data Base (SRDB) database (Bond-Lamberty & Thomson, 2010) on the annual litter carbon ux from each of the biomes corresponding to the location of studies in the meta-analysis. These data allowed us to roughly estimate the carbon ux in each of these biomes attributable to litter mass loss due to photodegradation.
Statistical analysis The effect sizes, expressed as log response ratio (lnRR) of mass loss, were computed with the function escalc() from the package 'metafor' (ver. 2. 1-0) (Viechtbauer, 2019), which uses sample sizes, standard deviations and means of the original studies and presents bias correction for small sampling. For each study, we selected only the nal collection date to avoid the potential issue of time-dependent effect sizes. We used a three-level mixed effect model using study ID and effect size ID as random factors as described in  (Assink & Wibbelink, 2016) , with categorical variables "Ecosystem", "Decay", "Climate", "Habit", "Life form" and "Latitude" as xed factors. The use of multilevel modelling in meta-analyses is a robust method for dealing with the problem of dependent effect sizes  (Assink & Wibbelink, 2016; Cheung, 2014; Noortgate et al.
, 2013). We used this method to test the overall effect of exclusion of each spectral region and the effect of the categorical variables with the function rma. mv() from the package 'metafor' (ver. 2. 1-0)  (Viechtbauer, 2019) , employing the Knapp and Hartung correction method for random metaanalyses  (Assink & Wibbelink, 2016; Knapp & Hartung, 2003). From these models we obtained the estimated average lnRR which we used to calculate the percentage change to better interpret the magnitude effect with the formula from Pustejovsky, 2018. Following the same multi-level approach, we analysed the correlation between the rate of photodegradation (effect size = lnRR) and the initial litter traits, climatic variables during the study period and absolute latitude, using them as continuous moderators in the model.
The litter traits considered were those initial traits reported in each study: carbon content (C); nitrogen content (N); carbon to nitrogen ratio (C:N); lignin content; lignin to nitrogen ratio (Lig:N) and speci c leaf area (SLA). The climatic variables used were average (Tmean), minimum (Tmin) and maximum temperature (Tmax), and cumulative precipitation (PP), over the study period. These data were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program (Sparks, 2018) using the "nasapower" R package version 3. 0. 1 (Sparks, 2020). The paucity of published studies from certain climates, ecosystems, latitude, etc. has the potential to introduce bias into the meta-analysis. To assess the risk of bias, we explored the dataset of retained studies to identify over-and under-represented categories.
Bias analysis and bias exploration We did not nd bias in any of the datasets used to calculate the effect of each spectral region: full- Most studies were carried out at latitudes between 30° and 50°North and South, while data from high latitudes were lacking (Fig. 1b ). Grassland and shrubland ecosystems were more studied than woodlands and open areas (Fig. 1c ). Dry climates were the most studied, while polar and tropical climates were the least studied (Fig. 1d ). In terms of the decay period, the rst 12 months of decomposition were the most studied (Fig. 1e ). The studies were located in six biomes: "boreal forests/taiga", "deserts and xeric shrublands", "Mediterranean forests, woodlands and scrub", "montane grasslands and shrublands", "temperate broadleaf and mixed forests", "temperate grasslands, savannas and shrublands" and "tropical and subtropical moist broadleaf forests" (Figs.
However, unlike Day & Bliss, 2019 we did not identify UV-A radiation as the most effective spectral region driving carbon emission through photodegradation across all studies in our meta-analysis. This could be due to the fact that most studies testing the effect of UV-A radiation were located in cool moist temperate broadleaf forest biomes at high latitudes characterised by low UV-A radiation  (Aphalo et al. , 2012; Grifoni et al. , 2008). We estimated annual carbon ux from litter attributable to photodegradation driven by different spectral regions, applying the percentage contributed by photodegradation to the gross annual carbon ux lost from litter in each biome obtained from the SRDB dataset (Bond-Lamberty & Thomson, 2010). This produced an estimate of photodegradation driven by the full spectrum of sunlight of up to 5-61 g C m - 2 per year according to biome type (Table  4 ), while blue light would potentially be responsible for 4-55 g C m - 2 per year according to biome type (Table  4 ).
74 = 0. 265, p-value = 0. 608); blue light (F 1,49 = 0. 336, p-value = 0. 565); UV-A radiation (F 1,32 = 0. 226, p-value = 0. 638); UV-B radiation (F 1,43 = 0. 03, p-value = 0. 857) and UV radiation (F 1,116 = 0. 345, pvalue = 0. 558). UV radiation was the most studied spectral region (20 studies, n = 118), while blue light (5 studies, n = 51) and UV-A radiation (5 studies, n = 34) were under-represented in our dataset (Fig. 1a).
Funding This research was funded by the  National Natural Science Foundation of China  ( 32122059 ), the  Chinese Academy of Sciences   Young Talents Program, and LiaoNing Revitalization Talents Program  ( XLYC2007016 ) to QWW, by  Chinese Academy of Sciences President's International Fellowship Initiative  ( 2022VCA0010 ) and the  Japan Society for the Promotion of Science (KAKENHI ,  17F17403 ) to QWW and HK, by  Academy of Finland  decisions # 266523 , # 304519  and # 324555  to TMR, personal  EF  project and a grant from the  Region "Haute-Normandie " through the  GRR-TERA SCALE (UFOSE Project ) to MP. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
spectrum (F 1 , 1 74 = 0. 265, p-value = 0. 608); blue light (F 1,49 = 0. 336, p-value = 0. 565); UV-A radiation (F 1,32 = 0. 226, p-value = 0. 638); UV-B radiation (F 1,43 = 0. 03, p-value = 0. 857) and UV radiation (F 1,116 = 0. 345, pvalue = 0. 558). UV radiation was the most studied spectral region (20 studies, n = 118), while blue light (5 studies, n = 51) and UV-A radiation (5 studies, n = 34) were under-represented in our dataset (Fig. 1a).
DNA metabarcoding analyses of the region were carried out by AllGenetics & Biology SL. Bioinformatic analysis of NGS (Next Generation Sequencing) data was performed using the Microbial Genomics module (version 21. 1) workflow of the CLC Genomics workbench (version 21. 0. 3). Raw sequences were filtered to remove low-quality reads and then clustered into Operational Taxonomic Units (OTUs) at a 97 % cutoff for sequence similarity and classified against the non-redundant version of the SILVA SSU reference taxonomy database  [19].
Acknowledgements The authors acknowledge the financial support received from the  Spanish Ministry of Science and Innovation  through the project  ANTARES  ( PID2019-110346RB-C21 ), co-funded by  FEDER. T. Allegue  would also like to express his gratitude to the same Ministry for awarding a research scholarship ( BES-2014-069114 ). The authors belong to the  Galician Competitive Research Groups (GRC)   ED431C-2021/37 , a program co-funded by  FEDER.
During the tests, the applied axial force was measured using a pressure probe in the axial pressure circuit, and the axial displacement of the piston was measured using a linear variable differential transformer (LVDT). Using the initial dimensions of the sample, axial stress and strain were calculated from these values. Porosity change was also calculated from the conversion of pore volume change given by the displacement of the piston in the pore pressure intensifier. Finally, acoustic emissions (AEs) were recorded using a USB AE Node from Physical Acoustics and a piezoelectric transducer (with a frequency bandwidth of 200-900 kHz) attached to the piston. The software AEwin, with a detection threshold for an AE hit set at 28 dB, was used to monitor the AEs energy, that is, the area under each received AE waveform.
Acknowledgments We are grateful to  Bertrand Renaudie  and  Christophe Nevado  for the sample and thin section preparation, respectively, and to  Gilles Morvan  for his help using the SEM. The Boise thin section used to perform a grain size and shape analysis was kindly provided by  Teng-fong Wong. The first author acknowledges funding from the  Doctoral School at the University of Strasbourg. M. J. Heap  acknowledges support from the  Institut Universitaire de France (IUF).
Data management and statistical analysis One of the security guards had a high salivary production during the EBC collection, which contaminated some of his samples. This resulted in a very high NO 3 -concentration in these EBC samples. We considered this worker as an outlier and therefore these samples were excluded from the statistical analyses reported in this paper. Individuals' anion concentrations, nitrite/nitrate ratio as well as the sum of nitrite and nitrate (ΣNO x ) were first log-transformed. In a second step, these variables were analyzed using linear mixed effects models with the subject ID as a random effect. For each of these outcomes, we fitted a model including the job group, the day of the week (in order to detect a possible cumulative effect over the week) and the shift (pre-vs.
Acknowledgements The authors would like to thank all the volunteers who agreed to participate to this study, the company staff who helped with study planning and data collection, particularly  G. Carillo ,  S. Besançon ,  T. Ben Rayana  and the technicians of the  Régie Autonome des Transports Parisiens (RATP)  who helped with the exposure assessment. We greatly appreciate the chemical analyses performed by the  University Center of Legal Medicine, Lausanne -Geneva  (anions and metals).
Materials and methods Specimens of Apocorophium acutum examined come from three different localities (Fig. 1):  (1) Bône, Algeria, type locality of female specimens; (2) Trébeurden, Brittany, France, 5 km from Lanion River, which is the geographical area of the first description of male specimens, and (3) Arcachon Bay, France. The specimens were examined with a stereomicroscope and a compound microscope. Body length (BL) from the anterior margin of head to the posterior end of telson and eggs' size were measured with NIS-Elements Analysis software. Females from Arcachon Bay (stations "bouée 13" and "Arcachon harbor") with intact brood pouches were separated in order to measure their fecundity: the eggs were removed from the brood pouch of each female, counted, and their diameter measured. Specimens from a third location in Arcachon Bay (station "blockhaus") were used to evaluate the relationship between body length and gender features (male characteristics features, oostegite shape and presence of eggs).