dataesr/scientific-paragraphs-categorization huggingface dataset:
The Tofware software (version 2. 5. 10, TOFWERK AG; PTR module as distributed by Ionicon Analytik GmbH), running in the Igor Pro 6. 3 environment (version 6. 3, Wavemetrics Inc. ), was used for postprocessing the mass spectra (exact mass calibration, determination of the atomic composition and fitting of the peak detected). Ion assignment relies on both fast-GC measurements and examination of the memory effect (i. e. , the time required for signal stabilization after the switching of the valve). The combination of both approaches was particularly helpful to unambiguously attribute ions to different terpenes. These species are known to produce similar product ions  (Kari et al. , 2018; Kim et al. , 2009). Quantification was based on the online measurements. Mixing ratios were calculated using proton transfer rate constants k (cm 3 /s), the reaction time in the drift tube, and the experimentally determined relative ion transmission efficiency.
Since fungi can also emit some terpenic compounds such as limonene or camphor, we cannot exclude that some emissions also originate from microorganisms  (Effmert et al. , 2012). However, since litter terpene emissions exhibited high correlations between them, it is likely that most terpenes originate from needle terpene pools. The determination of the presence of microorganisms using phospholipid-derived fatty acids technics (PLFA) and their potential emission using the mVOC database  (Lemfack et al. , 2018)  could help understand the contribution of the microbial emission to the total emission of the litter. Six monoterpenes, including α-pinene, terpinolene, Δ-3-carene, limonene, sabinene, and myrcene, were detected in most of the emissions and represented the major part of the global emission.
This study was supported by the LITTERVOC project (Emission de composés organiques volatils par la litière des forêts méditerranéennes soumises au changement climatique) -funded by the French National program EC2CO (Ecosphère Continentale et Côtière) from CNRS and the Fédération de Recherche 3098 ECCOREV (ECosystèmes COntinentaux et Risques EnVironnementaux) -and the CORVOLIM project (Inventaire d'émissions de Composés ORganiques VOlatils par la LItière de la végétation Méditerranéenne : application à la qualité de l'air) funded by program BioDivMeX (BioDiversity of the Mediterranean experiment) of the meta-program MISTRALS (Mediterranean Integrated STudies at Regional And Local Scales). We thank Guillaume Simioni and INRAE for allowing us to work in the Fontblanche AnaEE platform. We thank Sylvie Dupouyet for litter collection and help during the litterbag setting, and Caroline Lecareux for the GC-MS measurements.
Rainfall and stream stage measurements Five tipping-bucket rain gauges (HOBO Ware, equipped with data loggers) were installed and rainfall was measured during the rainy seasons (i. e. June-October) for 2018-2020. Event rainfall (P, mm) was obtained by summing the 5-minute rainfall depth for each rainfall event. Maximum rainfall intensity over 30 min (I 30 , mm hr -1 ) was determined and the nearest rain gauge was used for each sub-catchment. Hydrological monitoring stations were established at the outlet of every sub-catchment. The monitoring stations have a sharpcrested rectangular weir (in five catchments) and a V-notch weir at one sub-catchment (Fig. 3 ). During rainy seasons, the runoff stage was recorded every five minutes using a staff gauge and automatic pressure-transducer divers.
Separating baseflow and quick flow A runoff event (Q) was defined as the time between the start of rainfall and the end of the recession of quickflow (Fig. 4 ). Baseflow (Q b , mm) was separated from quickflow (Q qf , mm) using a function 'BaseflowSeparation' in the EcoHydrology package in R sofware  (Fuka et al. , 2018) , which uses a digital recursive filter algorithm  (Gonzales et al. , 2009; Nathan and McMahon, 1990). The analysis was performed using Eq. 5 and Eq. 6: Q qf (t) = βQ qf (t-1) + (1 + β) 2 [ Q( t) -Q (t-1) ] (5) Fig. 3. Setup of weirs and diver/staff gauge at the outlet of the sub-catchments. Q b(t) = Q (t) -Q qf (t) (6) It considers a filtering parameter (β) and the number of times of the application of the filter. We used β = 0. 95 and a three pass filter (forward-backward-forward)  (Liu et al.
CN II was then adjusted to slope (CN IIα )  (Ajmal et al. , 2020; Sharpley and Williams, 1990 ) (Eq. 10): CN IIα = [ CN II (50 -0. 5CN II ) CN II + 75. 43 ] × [ 1 -e -7. 125 (α-0. 05) ] + CN II ( 10 ) CN I and CN III are computed using the conversion formula given by  (Hawkins et al. , 1985). CN I = CN II 2. 281 -0. 001281CN II (11) CN III = CN II 0. 427 + 0. 00573CN II ( 12 ) The NRCS-CN, which was developed using λ = 0. 2, was adjusted to equivalent CN for λ = 0. 05  (Ajmal et al. , 2020; Woodward et al. , 2003b ) (Eq. 13): CN 0. 05 = 100 1. 42 -0. 0042 CN 0. 2 (13) The second approach to curve number determination was based on measured rainfall-runoff datasets. For this, a non-linear leastsquares function (nls), which uses the Gauss-Newton algorithm  (Bates and Chambers, 1992)  was used in R sofware, and the two parameters (λ and S) were determined, and the corresponding CN and Q qf were calculated using Eq.
8 and Eq. 7, respectively, for the different AMCs. The least-squares fitting procedure generally takes the iteratively possible least square of residuals. Generally, CN is modelled for rainfall events exceeding 25 mm  (Hawkins, 1993) , but in this study, we considered events exceeding 10 mm because we found few events exceeding 25 mm. CNs calculated from the rainfall-runoff data were compared with the tabulated NRCS-CN and analysed in relation to catchment characteristics and management. The runoff prediction performance of the two methods (i. e. tabulated CN and rainfall-runoff data derived CN) was evaluated with the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R 2 )  (Moriasi et al. , 2007).
Data analyses Quickflow runoff coefficients (RC qf ) were determined as the fraction of event Q qf to event P. Furthermore, lag-time (Q L ) for each event was determined as the difference between the time to peak P and peak Q qf. ANOVA, correlation matrix, and principal component analysis (PCA) were analysed using the R program for statistical computing (R Core Team, 2020) to relate quickflow to rainfall and catchment characteristics. When analysing multiple variables, the critical p-value (α) was adjusted (Bonferroni correction) to reduce type I errors, which often increases with an increasing number of independent variables  (Curtin and Schulz, 1998) ; otherwise, a significance level of α = 0. 05 was used.
CN values from NRCS-CN table and least square fitting methods The CN from the NRCS-CN table of values ranged from 59 to 91, while the model-derived CN ranged from 63 to 96 (Table  3 ). The average abstraction ratio (λ) determined from the rainfall-runoff dataset was 0. 05. However, the λ value for 88 % of the observations  was zero.
Model performance analysis The CN fitted to the rainfall-runoff dataset demonstrated that it was best fitted at λ = 0. 01 and λ = 0, explaining approximately 57-69 % of the variability in the quickflow response (Fig. 11 ). However, the data fitted at λ = 0. 2 could explain only 49-63 % of the quickflow response. The table CN prediction also confirmed that the capability of the NRCS-CN model was less satisfactory at λ = 0. 2 (Table  4 ). The NRCS-CN model performance was relatively good for λ = 0. 05 and 0. 01, explaining 56-76 % of the variability in the quickflow response. The data-based models at λ = 0. 01 and λ = 0 produced comparable results. Compared to the other models, λ = Fig. 7. Correlation matrix of the variables I 30 (30-minute maximum intensity), P (event rainfall depth in mm), Q qf (event quickflow depth in mm), and RC qf (event runoff coefficient in %).
Acknowledgments The authors acknowledge the financial support provided by the  VLIR-UOS  through the  Institutional University Cooperation (IUC)  project of  Bahir Dar University, Ethiopia and Ghent University, Belgium. We are grateful to the  BDU-IUC  office staff for smoothly facilitating financial and purchase-related activities. We are particularly thankful to  Derbew Fente , who supported the project immensely during the field installation and monitoring activities. Banchigize Habesha ,  Amsalu Akanaw , and  Getachew Dagne  are also acknowledged for their unwavering support. Tadla Girmay  is acknowledged for her support in laboratory activities. Akanaw Abebaw , an expert at  Dera Woreda Agriculture and Natural Resources Department ;  Marelign Tigabu  and  Fente Yihune  local development agents at Gelawdiwos and  Shime Kebeles , respectively, are acknowledged for facilitating the initial activities of the project and organising data collectors.
The structural properties of the two filters were quantified (PP/ZPT filter vs. PP filter): the thickness (Z) was 1. 41 ± 0. 08 mm vs. 1. 46 ± 0. 12 mm (averages and standard deviations for N = 15 quantified by micrometers); the porosity was 91. 1 ± 0. 3% vs. 89. 0 ± 0. 8% (averages and ranges for N = 2 quantified by mercury intrusion with mercury porosimeter Autopore IV 9500); the fiber median diameter of the most effective filtering layer (i. e. , MB3) was 5. 1; 2. 9 µm vs. 4. 5; 2. 6 µm (median diameters; standard deviations for N = 100 fibers analyzed by picture analysis with ImageJ software from scanning electron microscopic observations with EOL JSM-5800LV); the basic weight was 136 g/m 2 vs. 139 g/m 2 (obtained from the manufacturer). The airflow resistance (K) of the new PP/ZPT and PP filters was quantified from filter pressure drop (∆P, Pa) measurements vs.
Study design, participants, and data collection We surveyed 3763 respondents at 20 different universities across 16 countries using a standardised questionnaire  (Barton et al. , 2020). We selected the universities based on ease of access through our professional networks and on the practical considerations of collecting large amounts of data. We designed the survey instrument in English but when needed, translated and administered it in the local language(s). The respondents were approached via an email containing a link to the survey and recruited by a combination of convenience sampling and systematic sampling  (Fricker, 2008). We used an online Google Form to administer the survey in all of the countries except China, where respondents answered via an online crowdsourcing platform (Wenjuanxing).
Following  Mangiafico (2016) , we interpreted the strengths of association based on Cramér's V as small (<0. 3), medium (0. 3 to <0. 5) or large (≥0. 5). All statistical analyses were performed using RStudio version 1. 2. 5042 and R version 4. 0. 0 (RStudio Team, 2016) and the packages let, stats, car, agricolae, and psych. The R scripts used for these analyses can be found in the supplementary material.
WS were concentrated by SPE using OASIS-HLB cartridges eluted with dichloromethane-MeOH (v:v) and then the extracts transferred into DMSO. A blank control using milli-Q in parallel to the samples. The blanks were negative in MELN cells. A dose-response curve of the reference compound (E2) was also prepared. Upon overnight exposure (18 h), 0. 3 mM of D-luciferin was added to the wells. After 5 min, the luminescence was measured in living cells for 2 s per well using luminometer (Synergy H4, Biotek). The RM and HM were tested at concentrations ranging from 0. 01 × EQS to 50 × EQS. After SPE, the WS extracts were tested at concentrations ranging from 0. 00252 REF to 13. 5 REF. Effect data were determined as a percentage of maximal luciferase activity induced by 2. 7 μg/L E2, after subtraction of the background signal in untreated cells.
Dosedependent responses ranging from 0. 01 to 200 × EQS, 0. 2 to 4000 × EQS, and 0. 04-20 REF for the RM, HM and WS (respectively) were investigated. The data were fitted to a sigmoidal one-site competition four parameters logistic curve with GraphPad Prism version 8. 4. 3 (GraphPad Software, USA). The fit provided the IC 10 and IC 50 value (concentration of test compound, mixture or WS required to reduce the maximum polarization value to 90% and 50%).
Data evaluation Biological activities (i. e. receptor transactivation or receptor binding) of the RM, HM, and WS extracts were determined by the in vitro bioassays and expressed as biological equivalence concentrations (BEQ), representing the concentration of the positive control which elicits the same effect as the mixture or the WS (e. g. E2-equivalent concentration (EEQ))  (Escher et al. , 2014; Kunz et al. , 2017; Simon et al. , 2019)  according to the individual protocols of the test performers. To derive BEQ, concentration-response relationships of reference compounds and a dilution series of the mixtures, WS extracts and blanks were fitted to a four parameter non-linear regression curve (GraphPad Prism version 8. 4. 3) as previously described for (LYES, ERα-CALUX, hERα-HeLa-9903 and MELN)  (Escher et al.
The RM tested were Mix14, Mix19, Mix14NWL and Mix19NWL. All substances in the above-mentioned mixtures are present at 10,000 × EQS. Three WS were also tested, WS1 and WS2 are environmental samples, while WS-HM is an artificial sample reconstituted by spiking 1 L water with the HM (1000 × EQS) and enriched prior to bioanalyses as described in Section 2. 1. 1. For each bioassay, RM and WS, dose-response curves were determined and EC 10 and EC 50 or IC 10 and IC 50 values (in the case of LiBERA) were interpolated or extrapolated from the curves. E2 was used as a reference to determine the estrogenic potency of the RM, HM and WS as described in the data evaluation section (Section 2. 3). The EC 10 , EC 50 or IC 50 and IC 10 values were used to determine the relative potency of the reference mixtures and WS to E2 by means of the EEQ (see Section 2.
com. , Laboratoire de Mathématiques Nicolas Oresme,  Tecchio et al. , 2016). The same threshold as  Romano et al. (2006)  and  MacBeth et al. (2012)  was used considering the differences between the datasets: negligible if the Cliff delta (| ∂Cliff |) was < 0. 147, low if < 0. 33, medium if < 0. 474, or strong if > 0. 474. The Cliff delta was used to compare single effect models ("reef only" and "climate change only" scenarios) to the initial reference BOWF model and to compare the REEF model with the cumulative impact scenarios (reef effect + climate change effect). This was applied to the throughflow/activity (input/output) of each compartment as well as on the ENA. The delta also allowed us to compare the additive effect variation ( ) with the cumulative effect variation (. If the difference was considered medium (| ∂Cliff | < 0.
Acknowledgements This work was funded by the  Normandy Region  ( RIN Trophi-Service  project), and was part of the  TROPHIK  project, which benefited from  France Energies Marines and State  financing managed by the  National Research Agency  under the  Investments for the Future program  (reference  ANR/FEM   EMR-ITE ANR-10-IED-0006-12 ). NN, EA, FBRL and FFL are also supported by the  APPEAL  project ( ANR-10-IED-0006-25 ). We also thank for their help in compiling the datasets and for giving expert advice  Jeremy Lobry ,  Géraldine Lassalle ,  Mathilda Haraldsson  and  Maud Thermes  and all the partners and collaborators of the  TROPHIK  project for their help in compiling the datasets and for giving expert advice. We thank  Annie Buchwalter  for English correction.
Five replicates per sample were measured following the sequence standardsample-standard bracketing and the results were expressed as delta value using the Eq. (  1 ). These results were compared with the results obtained via fs-LA-MC-ICP-MS, which are published in ref. [30]. That is an alternative method in which 1 μL samples are deposited and dried in silicon wafers, in which micro-wells were previously produced. Results obtained for both analyses are graphed in Fig. 5. The correlation equation was obtained using the software Origin Pro2019b and it is y = (0. 89194 ± 0. 21196) x + (0. 09455 ± 0. 11821), which indicates that significant difference between both methods cannot be established at the 95% confidence level. The conclusions obtained with ETV-MC-ICP-MS in terms of the interpretation of the results would be analogous to those reported in ref.
Introduction Forest height is a significant forest biophysical parameter which could be used to derive important information about forest ecosystems, such as forest above ground biomass (AGB). Remote sensing technologies offer different techniques for retrieval of this parameter including Light Detection And Ranging (LiDAR) and Polarimetric Interferometric Synthetic Aperture Radar  (PolInSAR). LiDAR is able to measure the forest canopy height directly and it provides the most precise measurements in compare to other remote sensing techniques. The use of LiDAR data for forest canopy height mapping is well demonstrated and established in the literature  (Dubayah and Drake, 2000 , Simard et al. , 2011 , Fayad et al. , 2016 , Silva et al. , 2018). Nevertheless, LiDAR availability, particularly on airborne platforms, is limited by the acquisitions cost and persistent cloud cover especially in the tropics.
PolInSAR is a model-based technique for estimation of forest canopy height  (Cloude and Papathanassiou, 1997, Cloude and Papathanassiou, 1998). It uses two polarimetric SAR (PolSAR) images that are acquired over a test site with a given temporal and spatial baselines. Besides the challenges associated with temporal and volume decorrelation  (Kugler et al. , 2015, Lavalle and Hensley, 2012)  affecting the performance of PolInSAR model, current applications of PolInSAR are constrained by the limited availability of spaceborne PolInSAR data  (Pourshamsi et al. , 2018a). Currently there are data sources from spaceborne SAR interferometers such as Sentinel-1A and B (C-band;  (Torres et al. , 2012) , Tandem-X (X-band  (Krieger et al. , 2007) , RADARSAT-2 (C-band;  (Khati, et al. , 2018) ), and ALOS-PALSAR2 (L-band;  (Shimada et al.
RADARSAT-2 and Sentinel-1A and B offer a SAR C-Band which is again categorised as short SAR wavelength. Likewise X-Band SAR, it is also suffered by the canopy penetration capability for the tropical forest. In addition to this limitation, their data are acquired in a repeat-pass interferometric mode. Hence, their data are highly influenced by the volume and temporal decorrelations that significantly lead to a loss of coherence, which then cause a large bias in the height estimation of the tropical forest  (Khati, et al. , 2018). PolInSAR data from ALOS-PALSAR2 sensor are also available. However, similar to RADARSAT-2 and Sentinel-1A and B data, its data are acquired in a repeat-pass mode  (Khati, et al. , 2018). Considering the issues associated with the current techniques for forest height estimation and the availability of the required data, there are needs for new approaches for forest canopy height estimation.
An example is merging multi-source remotely sensed data such as PolSAR and LiDAR. PolSAR data provide important information about the physical scattering mechanisms  (Lee and Pottier, 2009) , but they do not allow direct retrieval of vertical scattering profiles. Hence, they should be combined with other data sources that provide vertical scattering profiles inside forest canopies such as LiDAR. This could be conducted in a machine learning approach. Therefore, in this study the potential of integrating PolSAR and sparse LiDAR datasets using machine learning algorithm for estimating tropical forest canopy height is demonstrated. Recently, numerous machine learning algorithms methods have been used for combining multisource remotely sensed data due to accurate classification/regression capability, less parameter tuning and lower computational complexity, higher ability to integrate multisource data, or no assumptions made on the data distribution among others.
The most popular and widely used machine learning methods are Decision Trees (DT) ensemble methods (e. g. Random Forest (RF)) and Support Vector Machine (SVM)  (Smola and Schölkopf, 2004). DT ensembles capture non-linearity between predictors and response, and also deal with highly correlated features. They can run on large-scale datasets in an efficient way and are robust to outliers and noise. SVM is a supervised classifier which explicitly looks for the best separating line (hyperplane) in the multi-dimensional dataset  (Smola and Schölkopf, 2004)  for both regression and classification tasks. The support vectors attempt to find an optimal regression that deviates from the target (training input) less that a given threshold ε while being as flat as possible  (Smola and Schölkopf, 2004).
The applications of machine learning algorithms for merging multisource remotely sensed data for forestry applications have been well established and documented in the scientific literature. They cover various applications such as forest health  (Wang et al. , 2015) , forest fire  (Zhao et al. , 2011) , forest change  (Singh et al. , 2014) , and forest biomass  (Gleason and Im, 2012; Fassnacht et al. , 2014) , etc. Although there have been many studies focused on various forestry applications (e. g. forest biomass estimation) using machine learning, only a small attempt has been made for forest height estimation. Stojanova et al. , 2010  investigated forest canopy height mapping by combining LiDAR and Landsat data using ensemble methods and single-and multi-target regression trees. Chen et al.
, 2012  suggested a joint use of LiDAR and Quickbird imagery in a Support Vector Regression (SVR) model to estimate forest canopy height over a test site in Quebec, Canada. Gu et al. , 2018  explored forest height mapping using a combination of Geometric-Optical Model for Sloping Terrains (GOST), Landsat 7 ETM+, and airborne LiDAR in the western Greater Khingan Mountains of China using neural network and a look-up table. García et al. (2018)  proposed a forest canopy height modelling technique by merging LiDAR, multispectral data and SAR backscatter using SVM where SAR backscatter and multispectral data were used to extrapolate LiDAR measurements and estimate forest canopy height at a larger scale. Li et al. , 2020  proposed merging the ICESat-2 satellite LiDAR data with Sentinel-1 (SAR C-Band), Sentinel-2 (Optical) and Landsat-8 data using Deep Learning and Random Forest.
Brigot et al. , 2019  investigated the potential of fusing PolInSAR data with LiDAR height using Random Forest and Neural Network. Xie et al. , 2019  proposed a multi-baseline PolInSAR and LiDAR data merging approach for improved forest height estimation. In a similar study,  Pourshamsi et al. (2018a)  examined an integration of PolInSAR components and LiDAR-derived height using SVM for improved estimation of forest height. Whilst the results obtained from this approach are more accurate than PolInSAR alone, the model performance is limited by the PolInSAR data availability. Pourshamsi et al. , (2018b)  investigated the combined use of polarimetric SAR parameters extracted from different decomposition techniques (H/A/Alpha  (Cloude and Pottier, 1996)  and Krogager  (Krogager, 1990)  and LiDAR samples to estimate forest height using SVM.
The estimated height has a significant accuracy and is relatively similar to the one estimated by the PolInSAR technique  (Pourshamsi et al. , 2018c). Although their results are significant and showed the potential of the approach for accurate forest height estimation in the absence of interferometric measurements, the LiDAR samples for training the model were collected from all part of the scene. This large distribution of LiDAR samples overlapping the entire SAR image is not often available. This underlines the need for examination of the approach with a different data sampling scenario for training the model. Additionally, in their study only the performance of the SVM is assessed. So, there is a need to assess the effectiveness of the application (PolSAR and LiDAR data integration for forest height estimation) by examining it in different machine learning models.