dataesr/scientific-paragraphs-categorization huggingface dataset:
The chlorophyll-retrieval is done in three steps. (3) where p is a unitless tuning parameter. Third, chl a concentration is computed by division with the chlorophyll-specific absorption coefficient at 665 nm, a * phy (665): [chl a] = a phy (665)/a * phy (665) (4) It is assumed in Equation (  3 ) that b b (λ) is spectrally neutral between 665 and 783 nm, and that at 665 nm the absorption by chlorophyll a and by pure seawater is much higher than the absorption by mineral particles and CDOM. The parameters a * phy (665) and p were initially estimated using a large dataset of field measurements from diverse inland, estuarine and coastal waters  (Gons, 1999; Gons et al. , 2002 Gons et al. , , 2005)). For our study site in Bourgneuf Bay, a * phy (665) was recalibrated to 0. 133 m 2 (mg chl a) -1 (standard error is 0.
Atmospheric Correction Ortho-rectified, geo-located, and radiometrically calibrated top-of-atmosphere (TOA) reflectance Sentinel2 images were downloaded in the SAFE format from the US Geological Survey web portal (https://earthexplorer. usgs. gov). A single scene can contain multiple granules (sub-tiles), but the USGS web portal makes it possible to directly download Sentinel2 data at granule level, thus reducing downloading and processing time. Sentinel2 TOA data was processed using the ACOLITE software (http://odnature. naturalsciences. be/remsem/softwareand-data/acolite) to derive the water-leaving radiance. This software proposes two options for the atmospheric correction (AC): (i) the NIR algorithm based on the assumption of spatial homogeneity of the red/NIR ratio for aerosol and marine reflectance  (Ruddick et al.
Due to the low signal in the SWIR wavelengths, a spatial smoothing filtering for these bands was performed  (Vanhellemont and Ruddick, 2016)  The final output of ACOLITE software is the ρ w (λ) data in Network Common Data Form (NetCDF). SPM and chl a concentrations were then computed from ρ w (λ) using the R project for statistical computing (R Development Core  Team, 2008).
The dataset was then divided in two subsets according to the tidal amplitude so that images were either clustered into neap tide (tidal amplitude <4 m) or into spring tide (tidal amplitude >4 m). Irrespective of their acquisition date, Sentinel2 data were tidally sorted from ebb tide to flow tide according to the time difference between satellite observation and low tide, and to the water height at the time of acquisition. Sentinel2-derived SPM and chl a concentration maps were thus clustered in 2 composite tidal cycles, either representative of neap or spring tide. In order to investigate the influence of changes in SPM and    chl a concentration on oysters, several physiological functions were directly retrieved from satellite data, as described below.
ACKNOWLEDGMENTS The  European Space Agency  is acknowledged for the production and distribution of Sentinel2 data. The  US Geological Survey  is thanked for maintaining the earth explorer web portal (https://earthexplorer. usgs. gov). Morgane Larnicol  and  Stéfani Novoa  are thanked for their participation to field work. The authors thank  Quinten Vanhellemont  and  Kevin Ruddick  for making the ACOLITE software freely available. The organizers of the CLEO workshop and special issue are thanked for their efforts to federate the ocean color scientific community. Emmanuel Devred  and  Zhubin Zheng  are thanked for their comments.
FUNDING This work has been supported by the " Programme National de Télédétection Spatiale " (PNTS, http://www. insu. cnrs. fr/pnts) in the frame of the  TURBO  project (grant n  • PNTS-2015-07 ), by the  French Research National Agency (ANR)  in the frame of the  GIGASSAT  project (grant n •  ANR-12-AGRO-0001 , http://www. gigassat. org/),by the  European Union 's  Research and Innovation FP7 program  in the frame of the  HIGHROC  project (grant n • 606797, http://www. highroc. eu/),and by the  Tools for Assessment and Planning of Aquaculture Sustainability (TAPAS)  project, a  Horizon 2020 Research and Innovation Action  funded by the  European Commission  (Grant agreement No:  678396 , http://tapas-h2020. eu/).
In the present study, we introduce a model suitable for the classification of grasslands using satellite image time series (SITS) with a high number of spectro-temporal variables (e. g. , Sentinel-2 data). Two temporal scales are considered in this work: (i) an inter-annual time series of three years to discriminate old grasslands from young grasslands and (ii) an intra-annual time series to identify the management practices. Note that in this work, the objects are not found from segmentation  [38]  but from existing dataset in a polygon form. The first contribution of this study is to model a grassland at the object level while accounting for the spectral variability within a grassland. We consider that the distribution of the pixel spectral reflectance in a given grassland can be modeled by a Gaussian distribution.
The second contribution is to propose a measure of similarity between two Gaussian distributions that is robust to the high dimension of the data. This method is based on the use of covariance through mean maps. The last contribution is the application of the method to old and young grasslands discrimination and of management practices classification, which are non common applications in remote sensing. Moreover, in our knowledge, mean maps have not yet been used on Gaussian distributions for supervised classification of SITS at the object level. In the next section, the materials used for the experimental part of this study are presented. Then the methods, including the different types of grassland modeling and the measures of similarity between distributions are introduced in section 3. Following that, we experiment the proposed methods on the classification of a real dataset in section 4.
Old and young grasslands In this study, "old" grasslands are 14 years old or more, whereas "young" grasslands are less than five years old. The French agricultural land use database (Registre Parcellaire Graphique) was used to extract the grasslands depending on their age. It registers on an annual basis the cultivated areas declared by the farmers in a GIS. Grasslands are declared as "permanent" or "temporary". Permanent grasslands are at least five years old, whereas temporary grasslands are less than five years old (Commission Regulation EU No 796/2004). For every plot declared as a grassland in 2014, its age was computed from the previous years declarations. We kept only the grasslands which were at least 14 years old in 2014 ("old"), and the grasslands which were less than 5 years old in 2014 ("young").
Management practices The information of the agricultural practices performed in the crops are not featured in the land use database. Therefore, this dataset comes exclusively from field data. As mentioned in the introduction, ground data is difficult to obtain in ecology since field work is fastidious. A field survey was conducted in May 2015 to determine the past and current management practices of 52 grasslands by interviewing the farmers or grasslands' owners. The practices remained stable for the years 2013 and 2014. Four management types during a vegetation cycle were identified: one mowing, two mowings, grazing and mixed (mowing then grazing). We eliminated the type "two mowings" of the dataset because of its under-representation (only three grasslands). The management types were used as classes for the classification (Table  2 ).
Classification protocol We compared the efficiency in terms of classification accuracy and processing time of all the presented methods by classifying the two grassland datasets on inter-annual and intra-annual time series (section 2). For each method, a Monte Carlo procedure was performed on 100 runs. For each run, the dataset was split randomly into training and testing datasets (75% for training and 25% for testing), preserving the initial proportions of each class. The same grasslands were selected for a given Monte Carlo repetition regardless of the method. During each repetition, the optimal parameters were tuned by cross-validation based on the best F1 score. Table  4  contains the parameters grid search for all the methods. Note that a wide grid was searched for the parameter α of αGMK to further analyze the distribution of selected values.
The penalty parameter C of the SVM process was chosen empirically and fixed to C = 10, after running several simulations. The classification accuracy for each repetition was assessed by the F1 score computed from the confusion matrix. The overall accuracy (OA) was computed but it is not presented here, because it does not reflect well the accuracy of the classification since unbalanced datasets were used. In order to compare each pair of methods, a Wilcoxon rank-sum test was processed on the pair of distributions of the 100 F1 scores. This nonparametric test is designed for two independent samples that are not assumed to be normally distributed  [84]. It tests if the two samples are drawn from populations having the same distribution. The kernels and the SVM were implemented in Python through the Scikit library  [85].
Management practices: intra-annual time series The classifications accuracies for management practices are shown in Figure  10  (F1 score) and in Figure  S3  in the supplementary materials (Kappa coefficient) for year 2013 and for year 2014. In terms of classification accuracy, methods based on divergences (BD and HDKLD) provided the worst results. Pixel-based methods, mean modeling method and mean generative kernel methods provided similar results in terms of F1 score, except for PMV which was significantly worse than the others for the year 2013. αGMK provided the highest values in 2013 (average F1 of 0. 65) but it was not significantly better than the others for this dataset. Indeed, due to the very low number of grasslands composing this dataset, confusion matrices were quite similar whatever the method.
It is therefore difficult to compare the methods efficiency in this configuration. Nevertheless, this dataset makes possible the comparison in terms of processing times, because the same spectral information was used for all the methods. Figure  11  illustrates the training processing time relative to the one of PMV versus the average F1 score for each method. In terms of computational time, the pixel-based methods required the largest processing times. BD was also very long, mainly because of the shrinkage procedure. Mean modeling was the fastest, followed closely by HDKLD. αGMK and GMK were equivalent in terms of computational times. For this configuration with a low number of grasslands, the mean modeling was the most efficient in terms of accuracy/processing time ratio. It is worth noting that the times series of 2014 produced higher classification accuracies (maximum F1 average of 0.
Methods efficiency The methods efficiency are discussed for the old and young grasslands classification, since the results provided with the other dataset are not significantly different, mostly because of the small dataset size. The divergence methods (BD and HDKLD) provided the worst results, showing that they are not robust enough to a high dimensional space. Although they provided results close to the best results, pixel-based methods (PMV and EMK) are the most demanding in terms of computational time and they do not scale well with the number of pixels. Indeed, they have to process N pixels instead of G grasslands with G N. Therefore, we had to reduce the number of pixels used for the classification. Using them on a large area might be difficult, as the old and young grasslands dataset showed.
Grassland typology On the whole, the classification did not reach high accuracies (F1 maximum average of 0. 73 for management practices and of 0. 71 for old and young grasslands classification). This can be explained by the unbalanced dataset with under-representation of grazing and mixed grasslands in the first application and under-representation of old grasslands in the second one. These classes obtained the lowest producer and user accuracies (cf. Tables  S1  and S2  in the supplementary materials) because of their limited number of samples for training the models. The methods should be tested on a more balanced dataset of grasslands classes. Moreover, as many times emphasized, semi-natural grasslands (which are present in these datasets) are characterized by their high level of heterogeneity.
Prediction of management practices on the land use database grasslands To show the efficiency of αGMK, we classified all the grasslands from the French agricultural land use database (RPG) covered by the Formosat-2 time series to predict their management practice in 2014. All the plots declared as grasslands in 2014, i. e. , "permanent grassland" and "temporary grassland" regardless of their age, were selected. After applying a negative buffer of 8m and rasterizing the polygons, we removed the plots representing less than 10 Formosat-2 pixels. In the end, there were 797 grassland plots covered by the extent of Formosat-2 for a total of 252,472 pixels. The multispectral SITS of 2014 was used. The SVM was trained on the whole field data (section 2. 3. 2) using the same grid search as in the experiments.
The parameters chosen after cross-validation based on F1 score were α = 5 and γ = 2 -15. Then, the model was used to predict the management practices of the 797 grasslands of the land use database. The classification accuracy could not be assessed since the true labels of the grasslands are not known. However, as described in the study site, a spatial distribution of the classes could be expected. Indeed, grazed and mixed grasslands should be found in the south-west of the site, whereas more mown grasslands should be in the north. An extract of the classification result is shown in Figure  13. It represents the classified grasslands in their raster format. As expected, most of the grazed and mixed grasslands are located in the south-west of the image, whereas the north of the image is mostly composed of mown grasslands.
Therefore, αGMK was very likely able to classify with an acceptable accuracy the grasslands management practices without any a priori geographic information. However, specific care should be considered, as not all the possible management practices were predicted. For instance, grasslands mown twice or unused grasslands were not in the training dataset, but it does not mean these managements do not exist in the rest of the data. The method deserves to be tested with an exhaustive grassland typology to produce more detailed grasslands maps. In terms of processing times, the proposed method is able to classify 800 grasslands, representing more than 250,000 pixels, at the object level from a high spatial resolution SITS within a few seconds on a conventional personal computer.
Indeed, this new kernel offers very low computational load. It can therefore be applied on a large dataset. With this kernel, we were able to process and to classify more than 250,000 pixels on a conventional personal computer within a few seconds. Even if it is the first application of generative mean kernels in remote sensing classification, the αGMK proved its efficiency and stability in these experiments. It is a good compromise between processing speed and accuracy for the classification of grasslands. The αGMK deserves to be tested on a larger dataset with more balanced classes. Seasonal statistics could be used to improve the representation of grassland phenology. These ideas will be considered in the future. This method was designed to deal with the dense SITS which will be provided by Sentinel-2 and to efficiently produce maps from this type of data.
Acknowledgments: This work was partially supported by a  CJS INRA-INRIA  contract and by the grant Défi Mastodons-CNRS. The authors would like to thank  CNES  and  CESBIO  for providing the pre-processed Formosat-2 data. Special thanks to  M. Lang  for playing a major role in the field work, to  D. Dallery  for designing the processing chain to compute the grasslands age from the RPG, and to  R. Carrié  for his careful reviewing of the introduction. We would like to thank the anonymous reviewers for their constructive comments.
Finally, we found that the number of consultations was associated with an increase in the chance of receiving a suspected cancer diagnosis. Therefore, GP are more likely to suspect cancer when symptoms are present for a certain amount of time and are not alleviated by symptomatic treatments. This study also shows that time is important for physicians so that they can develop a better understanding of their patients' potential diseases. This study was subject to several limitations. In general, retrospective primary care database analyses are limited by the validity and completeness of the data on which they are based. The estimated share of 5% is relatively small. This study includes different cancer diagnoses and gynecologists rather than GPs are responsible for diagnosing breast and female genital organ cancers.
The main limitation is that suspected and confirmed cancer diagnoses relied solely on ICD codes entered by primary care physicians. A full list of diagnoses that were used by GPs to document the suspected cancer diagnoses was not available. It is possible that only symptoms (for example, bleeding or pain) and not cancer diagnoses are documented in the event of suspected cancer, so as not to trouble the patients. Furthermore, the database did not include any valid information pertaining to TNM classification. Data on socioeconomic status and lifestyle-related risk factors were also unavailable. Finally, information on the delay between first symptoms and presentation to a GP was lacking. The main strengths of this study are the number of patients and the types of cancer included in the analysis.
Database This retrospective study is based on data from the Disease Analyzer database (QuintilesIMS), which compiles demographic, clinical, and pharmaceutical data obtained in an anonymous format from computer systems used in clinical practices  [8]. The quality and exactness of the data (e. g. , diagnoses and drug prescriptions) are regularly assessed by QuintilesIMS. Using prescription statistics for several drugs and age groups for several diagnoses, the Disease Analyzer database was found to be a representative database of clinical practices in Germany  [8]. Finally, several studies focusing on cancer and using the same database have already been published  [9] [10] [11].
Study outcome and independent variables The main outcome measure of the study was the rate of suspected cancer diagnoses made by GP within one year prior to the index date. Patients with a suspected cancer diagnosis were further sent to specialists in order to exclude such diagnosis. Demographic data included age groups (≤ 40, 41-50, 51-60, 61-70, 71-80, and > 80 years), gender, health insurance coverage (private vs. statutory), and frequency of visits to physician within one year prior to the index date. Clinical data included different types of cancer (breast cancer (C50), female genital organ cancer (C51-58), prostate cancer (C61), lung cancer (C34), cancer of digestive organs (C15-C26), urinary tract cancer (C64-68), skin cancer (C43-44), brain tumors (C70, C71), lymphomas (C81-88), multiple myeloma and leukemia (C90-98), and all other cancer diagnoses included in a single group), diabetes (E10-14), coronary heart disease (I24, I25), hypertension (I10), hyperlipidemia (E78), heart failure (I50), liver diseases (K70-77), diseases of esophagus, stomach, and duodenum (K20-31), depression (F32, F33), and dementia (F01-F03, G30).
Selection of Candidate Genes Candidate genes for association analysis were selected from 42,688 differential SNPs, which resulted from a comparative RNAseq experiment between S. tuberosum genotype pools with high and low quantitative resistance to late blight  (Mosquera et al. , 2016). The 42,688 SNPs were distributed in 9,855 genes and showed significantly different allele frequencies between resistant and susceptible genotype pools. Candidate genes were selected using the following criteria in order of relevance: the level of significance for the differential expression between resistance and susceptible pools, using the p-value for differential frequency of the SNP allele, the number of differential SNPs per gene and the gene's position on the Solanaceae function map for pathogen resistance in the GABI (Genome Analysis of the Plant Biological System) Primary Database 2   (Meyer et al.
Collection of SNP Data Twenty-nine genes distributed on all chromosomes except chromosome IX (Table  1 ) were successfully amplified from genomic DNA of the 104 genotypes. Amplicons were custom sequenced at the Max-Planck-Genome-Center Cologne using the dideoxy chain-termination sequencing method, an ABI PRISM Dye Terminator Cycle Sequencing Ready Reaction Kit and an ABI PRISM 3730 automated DNA Sequencer (Applied Biosystems, Weiterstadt, Germany). Sets of 10 sequences were edited and aligned with DNASTAR software  (Burland, 2000) , and sequences flanking the SNPs in the 10 genotypes were called. The sequences flanking the selected SNPs were used to call the SNPs with Data Acquisition and Analysis Software DAx (Van Mierlo Software Consultancy, Eindhoven, Netherlands) in the 104 genotypes. Data from DAx software were exported to EXCEL  (Excel Office, 2007).
Gene Co-location with Potato QTL Physical Positions The position in the potato genome of an associated gene was compared with QTL reported for the chromosome where the associated gene was located. Sequences of markers flanking the reported QTL on the chromosomes III and VI  (Danan et al. , 2011)  were compared trough the BLAST algorithm against the potato genome sequence. Marker sequences were retrieved from the GABI Primary Database 4  and compared to the potato genome resource website in the blast-n option, with default parameters, against the Potato Genome Sequence Consortium (PGSC) S. tuberosum Group Phureja DM1-3 516R44 pseudomolecules (v4. 03)  (Xu et al. , 2011 ; Supplementary Table  3 ).
AUTHOR CONTRIBUTIONS MFÁ designed, planned, and carried out the experiments for genotyping, SNP data analysis, and association mapping. She prepared the draft of the manuscript. MA designed and carried out the phenotypic evaluation in field conditions. CeG addressed the scientific direction of the evaluation to late blight resistance. MD designed and carried out the phenotypic evaluation in field conditions. JJ-G carried out the data analysis of the differential transcriptome experiment and provided the list of candidate genes. ChG provided scientific direction and access to the differential transcriptome analysis data base and contributed to the data analysis. SNP genotyping was performed in her lab at the Max Planck Institute for Plant Breeding Research. She supervised the work and revised the manuscript.
Similarly, concatenating across genes in an RNAseq analysis precludes gene-specific estimation of transition matrices or accurate estimation of rate heterogeneity, because current software to estimate partition schemes (e. g. ,  Lanfear et al. , 2012)  is still in its infancy when it comes to efficiently handling thousands of genes. It is also insufficiently known how sensitive phylogenomic resolution is to combining different types of data in the same analyses. While these issues are not new (e. g. ''total evidence" debate from the 1990s; e. g. Bull et al. , 1993) , the ''big data" typical of modern approaches could require new strategies for assessing confidence. For example, resampling genes or loci rather than individual nucleotide sites (e. g. gene jackknifing) in a concatenated analysis could be more informative than traditional bootstrap or posterior probability analyses  (Irisarri et al.