dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the specificity of crop distributions and sensor bands, six ANN models (3 crops, maize, wheat and rice × 2 sensors, Landsat-8 and Sentinel-2A) were created for GAI prediction. The scikitlearn library was used for the ANN implementation  (Pedregosa et al. , 2011)  using an architecture composed of one input layer (v z , s z , RAA, and the 10 Sentinel-2A bands or the 6 Landsat-8 bands, see Table  2 ), one hidden layer with five neurons, and one output layer with single neuron. Default hyperparameters of Python's scikit-learn package MLPRegressor were used, with a hyperbolic tangent (tanh) activation function, a maximum number of 6000 iterations and a tolerance for the optimization of 1e-8.
The yellow rectangles denote the atmospheric window, simulations falling in which were excluded for analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) observations in the validation dataset for each crop, thus providing an adequate reference to benchmark the reliability of the two alternative methods based on Bayesian theory developed in this study (full HMC and HMC + ANN). In the SNAP algorithm, the ANNs for each sensor have the same architecture of two layers (with five neurons in the first layer using tangent sigmoid transfer functions and one neuron in the second with linear transfer function  (Weiss and Baret, 2016). The ANNs are trained over PROSAIL simulations where the model input variables are sampled from a prior joint probability density function derived from existing literature covering different vegetation types.
4. 2) and the HMC + ANN method (Section 2. 4. 3). reflectance for Landsat-8 will take 123 milliseconds by the emulator while needing 427,502 milliseconds by PROSAIL, in Python 3. 7 on a Desktop PC Intel® Core™ i5-1135G7 @ 2. 4GHz, 16GB RAM. This increase in computation speed benefits from the scikit-learn and numpy Python packages when constructing the ANN, as these packages have pre-compiled C routines that performs matrix algebra operations more efficiently.
The joint posterior distributions for these variables  show an appreciable correlation between C m , N and ALA that indicates a compensation effect when inverting PROSAIL to maximize the link between canopy reflectance and GAI priors. This effect is particularly strong between ALA and C m since both these variables determine the reflectance in the NIR bands. The inter-dependency of PROSAIL variables is a direct consequence of the ill-posed nature of the RTM inversion problem  (Combal et al. , 2003; Lauvernet et al. , 2008; Zurita-Milla et al. , 2015). The joint posterior distributions of the crop-specific variables N, C w , C m , h and ALA presented in Figs. 6 7 8 are given by the relationship between the observed reflectance and GAI prior over the training dataset, which is an approximation of the actual relationship between true GAI and reflectance for the crops considered.
Actually, these distributions are expected to compensate the intrinsic limitations of PRO-SAIL to model the reflectance of actual canopies, including the assumption on the random distribution of canopy elements, i. e. , ignoring possible clumping of elements  (López-Lozano et al. , 2007). Similarly, the retrieved distributions should be also influenced by possible biases in canopy reflectance observations -due to atmospheric correction-or in the GAI measurements over the calibration dataset. For these compensation effects, the resulting distributions of crop- specific PROSAIL variables may differ from the actual values of these variables measured in field experiments. The most probable values for ALA computed from the calibration step for all the three crops studied (Fig. 6 7 8 ) are within the 70 • -80 • range, which is higher than the values reported by previous studies for some of these crops (e.
GAI estimates from full HMC prediction Results show a satisfactory retrieval performance (Fig. 9 ) over the validation dataset, which is completely independent from the calibration one, with R 2 = 0. 67 and RMSE =1. 01 for maize, R 2 = 0. 76 and the 95% probability interval. For fourteen GAI measurements, reflectance observations were available from both Sentinel-2A and Landsat-8 platforms, allowing to compare their GAI estimates. A high consistency between GAI estimates from Landsat-8 and Sentinel-2A was found with R 2 = 0. 82 and RMSE = 0. 62 (Fig. 10 ), which proves the crossinstrumental robustness of the current methodology. This also demonstrates that the strong constraint imposed on the space of canopy realization when using the prior information for crop-specific variables (Table  4 ) appears very efficient.
The GAI predictions in Fig. 9  follow the 1:1 line with no substantial bias over the validation dataset. This indicates a good consistency between the calibration and validation datasets in terms of satellite calibration, GAI measurements and more globally on the distribution of crop characteristics. However, for some samples at specific validation sites, GAI estimates present a small, but systematic, deviation against the observed value. This is the case for some rice samples at one validation site Fig. 9c , in green). For this site located in the tropical region, the grown variety (Indica rice) presents lighter green leaves than the Japonica variety used in training and other validation sites (e. g. , the grey points) in the mid-northern latitudes. Similar canopy reflectance may be contributed by darker green leaves with less leaf area, and this consequently leads to the underestimation of GAI.
In the methodology proposed in this study, model input variables are first calibrated based on independent datasets, which allows to get an estimate of p(σ i ), see Fig. 12. That estimation of p(σ i ) from the calibration step is key as p(σ i ) is often unknown. In the absence of such information about model errors, previous studies  (Shiklomanov et al. , 2016)  adopted a non-informative inverse gamma prior distribution (also known as Jeffreys prior, see Eq. (  6 )), widely used for Bayesian methods  (Gelman, 2006). However, this non-informative prior distribution is not necessarily a good approximation of the actual model error. As Fig. 12  depicts, the most probable values for σ i resulting from the calibration step are close to 0. 01 for all the three crops. The non-informative gamma prior seems therefore over-confident with the reflectance modelled by PRO-SAIL, which will impact the estimated uncertainties.
Comparison of Bayesian against machine learning-based inversion methods for GAI retrieval: importance of choosing the right distributions for model training The results achieved by the proposed methodology (full HMC and HMC + ANN) are compared here against the SNAP reference algorithm (based on ANN), and the results from  Camacho et al. (2021) , using GPR trained with either ground data or PROSAIL to retrieve GAI. Great attention is paid to the distinction between effective and true GAI estimates, since both SNAP (Section 2. 5) and GPR provide effective GAI. The ground observations from the IMAGINES database acquired with DHP (Table  1 ) permit to compare true and effective GAI ground observations. Fig. 13  shows the results of this comparison, where the clumping parameter is the slope between effective and true GAI.
Surprisingly, performances degrade for maize, as Fig. 14  shows, where the clumping coefficient over-corrects the effective GAI values. These results highlight the importance of selecting the appropriate distributions of PROSAIL parameters to generate the training dataset and minimize prediction bias in ANN-based inversion algorithms. The crop-specific distributions of the input variables derived from the HMC calibration (Table  4 ) constitute an optimal training dataset for ANN that reproduces the observed relationship between reflectance and GAI, yielding satisfactory performances. The HMC calibration implicitly compensates the turbid medium assumption in PROSAIL by adapting the co-distributions of PROSAIL variables -possibly ALA, C m and N, see Section 3. 2-to approximate the relationship between the observed reflectance and true GAI.
Also for wheat, where the clumping effect is minor, thanks to the variables co-distributions resulting by the HMC calibration, the HMC + ANN performed better than the SNAP algorithm. Finally, we compared the previous performances to those reported in Conversely, the GPR trained with effective GAI ground measurements perform better for the three crops (Table  5 ). However, Camacho et al. (2021) considered effective GAI values, which would probably result in higher RMSE when estimating true GAI, at least for rice and maize that show larger clumping values. Further, the dataset used to estimate the performances is not the same, although many observations of their dataset are included in the IMAGINES database that was used in this study, thus making difficult a direct comparison between our results and those of  Camacho et al.
(2021). It seems clear that fully empirical approaches are, at least, as accurate as those based on RTM when the volume and diversity of the training dataset is sufficient. However, the main advantage of the Bayesian-based approach against the empirical ones is the calibration of crop-specific PROSAIL variables to minimize bias against GAI, providing an explicit insight of compensation effects among variables, especially for those difficult to be measured such as the hotspot of the leaf structure parameter. More importantly, these distributions used both in HMC and HMC + ANN are valid for any satellite platform -here we combined Landsat-8, Sentinel-2A and Quickbirdwhereas the coefficients of a full empirical model will be sensordependent. This is, indeed, a major advantage of the presented methodology, as it enables to fully exploit large observational datasets from multiple sensors, making unnecessary to perform cross-calibration exercises for combining observations from multiple sensors.
Conclusion This study proposes a novel approach based on the Bayesian theory and the PROSAIL radiative transfer model, to estimate true GAI with high-resolution satellite platforms. This approach relies on the hypothesis that some PROSAIL model input variables (e. g. leaf inclination, hotspot, leaf dry matter content, etc. ) are crop-specific and, therefore, they follow the same distribution for a given species. In the approach proposed, the Hamiltonian Monte Carlo (HMC) algorithm is first applied -in a calibration step-to retrieve the crop-specific PROSAIL variables for wheat, maize and rice using an extensive dataset of GAI measurements and satellite reflectance observations. Then, the resulting distributions of crop-specific are used -in a prediction step-to predict GAI from an independent set of reflectance observations.
The validation of the proposed methodology over a comprehensive dataset of GAI measurements for three major crops showed satisfactory accuracy of the corresponding estimates from satellite reflectance with RMSE of 1. 01, 1. 33 and 0. 97 for maize, wheat and rice, respectively. These estimation performances are better than those generally reported when using radiative transfer models that are non-crop-specific. However, they appear to be slightly behind those of purely empirical ones using machine learning trained for each crop over a large and diverse dataset as recently demonstrated by  Camacho et al. (2021) , but on effective GAI. The calibration of the crop-specific PROSAIL variables using the HMC algorithm permitted to retrieve, empirically, a set of distributions for these variables that help to minimize the bias between GAI estimations and ground measurements.
This ability paves the way for the harmonized use of multisource remote sensing data and can help building virtual constellations that monitor crop continuously with a high revisit frequency. The main limitation of Bayesian Monte Carlo methods is the long computation time required to estimate posterior distributions, despite the efforts devoted to emulate PROSAIL and making model runs more computationally efficient. This makes inadequate the use of the HMC algorithm to predict GAI over large sets of reflectance observations (e. g. applying on complete images). For that reason, an alternative approach to the full HMC was evaluated. In this approach, named HMC + ANN, we replaced the HMC algorithm in the prediction step by an ANN. The accuracy of the HMC + ANN approach was comparable to the full HMC and it is adequate to predict GAI over large datasets.
Acknowledgements The authors would like to thank  Dominique Fassbender  and  Luigi Nisini  ( Food Security Unit, Joint Research Centre, EC ) for their advice and interesting discussions on Bayesian data analysis. This work has been realized with the support of  MESO@LR-Platform at the University of Montpellier  (https://meso-lr. umontpellier. fr/), and the  Agro-EcoSystem department of INRAE  provides the funds to access the MESO@LR infrastructure. This work is jointly supported by the the  National Key Research and Development Program of China  ( 2016YFD0300101 ),  National Natural Science Foundation of China  (Nos. 41871253 ), " Taishan Scholar" Project of Shandong Province  (No. TSXZ201712 ),  Basic Research Project of Shandong Natural Science Foundation of China  (No. 2018GNC110025 ). The grant of the principal author was funded by the  Chinese Scholarship Council (CSC)  (No.
Resulting cycle sequence reactions were purified with ETOH/EDTA precipitation and were sequenced in-house at the UL Lafayette campus on an ABI Model 3130xl Genetic Analyzer. The resulting chromatograms were assembled and edited using Sequencher 5. 1 (Gene Codes Corp. , Ann Arbor, MI, USA) and exported as individual ". FASTA" files. Species identification, specimen collection information, and GenBank accession numbers are listed in Supplementary Table  1.
For both datasets, RAxML searches consisted of 1000 independent restarts with the above models and partition schemes with 1000 independent searches to find the tree with the lowest likelihood score and 1000 Bootstrap (BS) replications. The Newick file was imported into FigTree 1. 4. 2  (Rambaut and Drummond, 2012)  as a starting point for further editing in Microsoft Publisher (i. e. , tree structure export for label editing, respectively). The single gene alignments were 959 bp for psbA and 1,494 bp for rbcL, while the concatenated alignment was 2,453 bp. The psbA alignments included 19 sequences of Phymatolithon with 11 additional sequences in the Hapalidiales ingroup and two sequences in the Sporolithales outgroup; rbcL included 21 sequences of Phymatolithon and 11 additional sequences in the Hapalidiales ingroup and two sequences in the Sporolithales outgroup; and the concatenated tree of psbA and rbcL consisted of 32 sequences of Phymatolithon and 19 additional sequences in the Hapalidiales ingroup, and two sequences in the Sporolithales outgroup.
Estimation of evolutionary distances from nucleotide sequences was done in MEGA X using the Jukes-Cantor pairwise distance model. This analysis involved 34 nucleotide sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There were a total of 507 positions in the final dataset (Supplementary Table  2 ). Species delimitation analysis for each gene was performed separately using Assemble Species by Automatic Partitioning (ASAP)  (Puillandre et al. , 2021)  using the Jukes-Cantor substitution model for the following parameters: split groups below 0. 01 probability, highlighting genetic distances between 0. 005 and 0. 05.
Acknowledgments We greatly acknowledge the support from  NSF  research grant  DEB1754504. RK would like to thank the  Phycological Society of America Grant-In-Aid Research  and the  University of Louisiana at Lafayette Graduate Student Organization  for partially funding this research. We thank  Tom Pesacreta Mike Purpera , and  Lily Ann Hume  at the  UL Lafayette Microscopy Center  for their help with SEM imaging, and  Ally Nyugen  for help with PCR and initial sequencing.
g. , five out of 16,  Schumm et al. 2021) , and few geolocators were retrieved back containing full data (five out of 64,  Eraud et al. 2013). In addition, low recovery rates of ringed birds from the winter quarters  (Zwarts et al. 2009; Marx et al. 2016)  and difficulty in distinguishing between the subspecies S. t. turtur and S. t. arenicola on potentially shared wintering ranges complicate determining a complete picture of the wintering destinations  (Hanane 2017). However, information from previous research, ringing, and tracking studies are useful to help define plausible wintering areas for other methods, including probabilistic assignment to origin using stable isotopes  (Hobson et al. 2012). In this study, we used intrinsic isotopic markers in metabolically inert feathers grown during the wintering period to identify African non-breeding origins of turtle doves sampled during migration and breeding periods in Europe.
e. , 'stacked') across assignments for all individuals in the sample to represent potential origins for the entire sample set but also per sampled country. Manipulation of digital files and assignment to origin analyses were conducted using several packages including 'raster' 2. 5-8 (Hijmans 2016), 'mvnmle' 0. 1-11. 1 (Gross and Bates 2018), 'maptools' 0. 9-9 (Bivand and Lewin-Koh 2019), and 'rgdal' 1. 2-13  (Bivand et al. 2019)  in the R statistical computing environment 3. 5. 3 (R Core Team 2019). As stable isotope values of δ 2 H f were normally distributed (Shapiro-Wilk: W = 0. 991, p = 0. 326) but δ 13 C f were not (Shapiro-Wilk: δ 13 C f : W = 0. 861, P < 0. 005), we applied parametric and non-parametric statistics where appropriate. To assess the overlap between existing protected areas as well as hunting areas and winter quarters of turtle doves according to the dual-isotope multivariate probabilistic assignment, we clipped the obtained assignment with a spatial dataset of protected areas (mix of polygons and points) from UNEP-WCMC (2021).
Assignment probabilities of individuals (0 to 1) were summed according to the maximum value obtained in a pixel during the assignment process for the overall sample set representing the percent of individuals potentially originating from a cell in the isoscape. The assignment is restricted to a hitherto described turtle dove wintering range (outline; in red in the online version). (a) Overlap of likely wintering areas with internationally and nationally protected areas as well as bird sanctuaries, and (b) overlap with hunting areas. Information on protected and hunting areas was based on a dataset (mix of polygons and points) from UNEP-WCMC (2021).
Acknowledgements We are thankful to all the persons and organisations who helped during fieldwork, with logistics in the field and provided feather samples: France:  Marcel Rivière  (Oléron),  Bernard Mauvy (Auvergne) ,  Luc Tison  and  Hervé Bidault (Chizé) ,  Tony Presse ,  Alain Richard  and  Denis Koniska  (Marne); Italy:  Sara Riello ,  Elisa Mancuso ,  Riserva Naturale Statale Isole di Ventotene e S. Stefano and LIPU Centro di Recupero Fauna Selvatica di Roma ;  Malta: John J. Borg  ( NMNH, Heritage Malta ), the  Maltese Ringing Scheme and BirdLife Malta ;  Bulgaria: Strahil Peev. We thank  Yvonne Klaar ,  Anja Luckner , and  Karin Grassow  for help with the stable isotope analyses and  Steven L. van Wilgenburg  for contributing code and advice on assignments to origin.
Statistical analyses In order to understand variations in δ 15 N measurements, a linear mixed-effects model was calculated, with the taxon and period as fixed effects (independent variables) and cereal grain group as a random effect. We use a mixed model because samples from the same grain group are correlated, and considered to be repeated measurements from the same deposit. Grain groups are compared two by two with the nonparametric Wilcoxon rank-sum test. All statistical tests and models are evaluated at an alpha level of 10%. Statistical analyses were carried out using the statistical programming language R 4. 0. 2 (R Core Team, 2020). Models and their pseudo-R 2 were calculated with the nlme and the piecewiseSEM packages  (Pinheiro et al. , 2021; Lefcheck, 2016). Graphics were produced with the ggplot2 package  (Wickham, 2016).
Acknowledgments This research is a part of a PhD funded by the  DIM Région -Île-de-France  " Matériaux Anciens et Patrimoniaux" ("AGROFER " project, dir. V. Matterne,  M. Balasse  and  F. Malrain ). All analytical costs were supported by the  MNHN ATM ("AgroFertil " project  2020 ; dir  V. Matterne  and  M. Balasse ) and by the  Sorbonne Université OPUS programm  (" AGALITT " project  2019 , dir. M. Balasse  and  V. Matterne ). Stable isotope analyses were conducted at the  SSMIM (MNHN Paris ). We thank  Louise Byrne  for English editing.
Ocean acidification (OA) is a serious consequence of climate change with complex organism-to-ecosystem effects that have been observed through field observations but are mainly derived from experimental studies. Although OA trends and the resulting biological impacts are likely exacerbated in the semi-enclosed and highly populated Mediterranean Sea, some fundamental knowledge gaps still exist. These gaps are at tributed to both the uneven capacity for OA research that exists between Mediterranean countries, as well as to the subtle and long-term biological, physical and chemical interactions that define OA impacts. In this paper, we systematically analyzed the different aspects of OA research in the Mediterranean region based on two sources: the United Nation's International Atomic Energy Agency's (IAEA) Ocean Acidification International Coordination Center (OA-ICC) database, and an extensive survey.
A surprisingly large number of studies are devoted to OA in the Mediterranean Sea, mostly addressing its chemical and biological aspects  (MedECC, 2020, and references therein). The diversity of these studies, the various methodologies that have been adopted, and the importance of quantifying different aspects of OA requires standardization of best practices, and an effective dissemination of knowledge and investments in capacity building. All these key activities are addressed by the IAEA's Ocean Acidification International Coordination Centre (OA-ICC; Home: Ocean Acidification International Coordination Centre (OA-ICC) | IAEA) that promotes comprehensive OA data portals with publicly available data (https://www. iaea. org/services/oa-icc), and supports global and regional ocean acidification networks, including the Ocean Acidification Mediterranean-Hub (OA Med Hub), a Mediterranean network affiliated to the Global Ocean Acidification Observing Network (GOA-ON).
It is currently well recognized that the ocean holds the key to an equitable and sustainable planet and communities (UN Ocean Decade; https://www. oceandecade. org/). Nevertheless, this depends on the data produced by the scientific community. Consequently, producing reliable OA biogeochemical and biological data is crucial for the much needed ocean governance and mitigation of OA. In order to systematically evaluate the OA research status and gaps, and to give recommendations on the best ways to proceed in this field of research, bibliometric studies have proved to be insightful  (Nisumaa et al. , 2010; Yang et al. , 2016). The UN Ocean Decade (The Ocean Decade -The Science we need for the Ocean we want) provides a great opportunity to upgrade marine research to review, develop, and report on the best OA information needed by policymakers, so that the best sciencebased decisions can be made to protect our ocean and the communities that rely on a healthy and vibrant ocean.