dataesr/scientific-paragraphs-categorization huggingface dataset:
Their work permitted the development of a Land Data Assimilation System (LDAS). Albergel et al. [6]  further extended the LDAS at a global scale (LDAS-Monde) and showed that the assimilation of LAI observations can be used to analyze the root-zone soil moisture in addition to the vegetation above-ground biomass. This also means that LDAS-Monde could potentially use the information content of C-band backscatter observations (σ • ) to analyze vegetation variables together with the root-zone soil moisture  [6]. To achieve this goal, an observation operator such as the water cloud model (WCM)  [7]  is needed for predicting σ • from LSMs. SSM and LAI observations can be assimilated into the ISBA LSM because these two variables can be predicted by ISBA. It must be noted that the current version of ISBA is not able to simulate the VWC.
The ASCAT backscatter observations are available at full resolution, together with spatially weighted measurements at 25 km × 25 km or 50 km × 50 km resolution  [2, 17]. In this study, we used an ASCAT σ • dataset normalized to an incidence angle of 40 • , which was provided by the Vienna University of Technology. The dataset is sampled on a discrete global grid (12. 5 km × 12. 5 km) at a spatial resolution of 25 km × 25 km. The incidence angle normalization is done by using a second-order polynomial describing the relationship between incidence angle and backscatter  [18]. The σ • dataset consists of mean daily values incorporating data from ascending and descending orbits. In order to adapt the normalized ASCAT σ • data to the 0. 25 • × 0. 25 • grid used by the ISBA model, the data were interpolated by an arithmetic average to the model grid points.
Performance of the WCM The performance of the calibration of the WCM using the SCE-UA algorithm is assessed in Table  2. Simulated and observed σ • values are compared using a large number of observations (n) for the calibration and for the validation period, using the R and RMSD scores. For these two periods of time, n = 209,327 and n = 204,520 observations are available, respectively, after sorting out the soil freezing events. For each time period, two data subsets are considered in addition to the pooled dataset: springtime (i. e. , in March, April, and May (MAM)) and summertime (i. e. , in June, July and August (JJA)). Table  2  shows that the scores for the validation period are very close to those for the calibration period. The WCM performs better in the springtime in terms of correlation than in the summertime.
WCM performance: statistical scores of simulated σ • values over southwestern France. The calibration period of the parameters is from 2010 to 2013, and the validation period is from 2014 to 2016. For both calibration and validation periods, scores are given for the pooled dataset (All) and for the March, April, and May (MAM) spring period and the June, July, and August (JJA) summer period. The number of observations is indicated (n).
Location of shell  14  C age sample from ref. 73  that was omitted from our record is shown by red star, clearly well outside the high shoreline elevation (see Supplemental Information for details). Base hillshade was created using ArcGIS 10. 4 using the 10 m-resolution digital elevation model from the USGS National Elevation Dataset  90  (available from https://nationalmap. gov). snowfall  23. Since most paleoclimate archives considered here record changes in annual-average effective moisture, we focus on modern winter climate conditions to help interpret past hydroclimate changes. Winter hydroclimate in the western U. S. is strongly controlled by the mid-latitude westerly storm track that transports moisture from the Pacific Ocean via extratropical cyclones. Along the western margin of North America, precipitation associated with atmospheric rivers-narrow corridors of strong horizontal moisture transport located in the warm sector of extratropical cyclones  24  -contribute up to 50% of total precipitation during the cool season  25.
14 C dating. Each tufa or shell sample was reacted with 100% H 3 PO 4 and vacuum-extracted and purified  69. AMS and δ 13 C measurements were performed by the Arizona AMS Laboratory. Raw 14 C ages are reported with 1σ uncertainty and were calibrated using the OxCal 4. 2 software 71 with the IntCal13 calibration curve  72. All ages aggregated from previous publications  [41] [42] [43] [44] [45]  have been recalibrated using IntCal13 for consistency. All calibrated  14  C ages are reported in calendar years (cal yr BP) or calendar kiloyears before present (ka) as the median age of the calendar range plus or minus 2σ uncertainty. Constructing the Chewaucan lake level record. The lake level record is based on 73  14  C ages of shorezone tufa samples collected on bedrock outcrops and boulder paleo-shoreline deposits, and shells of mollusks (Vorticifex effusa, Pyrgylopsis sp, and Valvata humeralis) that lived in shallow water habitats.
This finding indicates the lake epilimnion was well mixed with the atmosphere at all times in the past so that tufas and shells of shallow-water mollusk taxa should yield accurate  14  C ages. Shells are composed of 100% aragonite (based on XRD analyses of select samples)  70  indicating post-depositional weathering has not affected them. Ages from tufa and shell are in good agreement during the deepest lake interval, providing additional confidence in the age dataset. Based on this evidence, we applied no reservoir correction. Compilation of paleohydrologic records. The compilation of 23 total paleohydrologic records (Table  S1 ; Fig. 1a ) were included for comparison to Lake Chewaucan and the TraCE-21k analysis. These include pollen-based vegetation records  1, 50, 76, 77  , shoreline-based lake level records  44, 52, 53, [78] [79] [80] [81] [82] [83] [84] [85] [86]  , lacustrine fauna (diatoms, ostracoda) moisture records  49, 51, 82  , other lake sediment-based hydrologic proxies  4, 87  , and desert wetlands discharge records  5, 15.
This compilation was chosen based on several criteria; the first being that they were sensitive primarily to effective moisture amount. Records that are less sensitive to effective moisture amount, such as glacier records (which respond strongly to temperature), and speleothem stable isotope records (which are also sensitive to temperature, moisture source, and seasonality) were omitted. This is with the exception of McLean's and Moaning Cave from the central Sierra Nevada, where trace element data were coupled with oxygen isotope data to provide a more robust hydrological signal  12. Records were also chosen based on their age model uncertainty, and the timespan covered during the Deglacial interval. Since the focus of our analysis was on the HS1b and B/A periods, continuous records (i. e.
core reconstructions) needed to cover the entirety of both intervals. Continuous records also needed sufficiently low age model uncertainty (generally ~400 calendar years, Table  S1 ) to clearly differentiate millennial-scale changes. For example, many pollen-based vegetation records produced before electronic datasets were widely available (so only the publication graphic of the record is available) and before routine calibration of  14  C age timescales, were omitted due to inability to translate those records objectively to a timescale comparable to more recent records. As the offset between calendar and 14 C years varies considerably between >1000 and >2000 years during the Deglacial interval  72  , these records have too much uncertainty to reliably define millennial scale changes. Age model uncertainty was estimated as the average 2 sigma uncertainty for all ages ( 14 C, U-Th series, or curve-matching) falling within the B/A and HS1 intervals (18.
Acknowledgements This work was funded by a  Desert Research Institute Jonathan O. Davis  grant to AMH, as well as  Comer Science and Education Foundation  grants to AMH and JQ. BJH was supported by a grant provided by the  Tides Foundation. We thank  Feng He  for providing TraCE-21k model output, and  Erica Wise  for the SOI-PTT correlation data displayed in Figure 1a. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
(b) Chewaucan drainage system. Drainage basin boundaries shown in black, approximate modern lake extents in light blue, B/A high shoreline extent shown in dark blue, LGM extent shown in green, early Holocene extent shown in yellow. 14  C sample locations are plotted by sample type. Named areas where samples were collected are labeled (e. g. the Abert Rim). Overflow point (1338 m asl) connecting the Summer Lake sub-basin and Abert Lake subbasin shown by yellow star. Location of shell 14  C age sample from ref. 73  that was omitted from our record is shown by red star, clearly well outside the high shoreline elevation (see Supplemental Information for details). Base hillshade was created using ArcGIS 10. 4 using the 10 m-resolution digital elevation model from the USGS National Elevation Dataset 90  (available from https://nationalmap.
| Test methods The adolescents and their parents were informed by a poster in the waiting room about an ongoing study on teenage health; as a general rule, no mention was made of suicidality. They signed an agreement to participate and kept the original document. When a minor attended the practice alone, a copy of the agreement was sent to a parent, who could refuse utilization of the data (except in Switzerland, where mature minors could consent to participation without a parent's involvement). The GP was asked to complete a paper-based questionnaire at the end of the consultation for each 13-to-18-year-old patient who met the inclusion criteria, without exception. The procedure to be followed was: 1 -The GP him or herself, not a secretary or a practice assistant, was to apply the BITS test.
Minimum sample size was 460 adolescents and 100 GPs. P-value <. 05 was considered as statistically significant. Estimates with 95% confidence intervals (95% CI) were applied in a version suited to cluster sampling, taking into account possible non-independence of the observations put forward by the same GP. Data capture was carried out by two different operators and followed by comparison of the files. SAS software (version 9. 4) was used.
| Explanation These findings confirm and even improve the results of our previous work: in the initial BITS study using a self-completed questionnaire among 15-year-old school students, sensitivity was 75% and specificity 70% (AUC = 0. 78)  (Binder et al. , 2018). In the TSTScafard detection utility study, sensitivity was 79%, and specificity 55%  (Binder et al. , 2018; Binder & Chabaud, 2007b). Our data are in line with those of questionnaires investigating suicidality (even though these questionnaires are designed to assess suicide risk per se, not to alert GPs about the need to ask about suicidality). In these different studies, sensitivity ranged from 52% to 87% and specificity from 60% to 85%  (O'Connor et al. , 2013; Perlman et al. , 2011). The rate of stated SA (boys 4%, girls 8%) was lower than the French benchmark data collected by questionnaires in school settings: (boys 7%, girls 15.
ACKNOWLEDGEMENTS We wish to thank the "Relais 17 association," which financed this study. We also thank for their collaboration  Daniel Marcelli  and  Ludovic Gicquel  of the " Conseil Scientifique ";  Michel David ,  Caroline Huas ,  Louis Jehel ,  Catherine Laporte ,  Nassir Messaadi ,  Patrick Tabouring  and  Louis Tandonnet  of the " Comité Scientifique " and as on-site correspondents;  Philippe Castéra ,  Paolo Di Patrizio ,  Jean-Marc Franco ,  Catherine Jung ,  Pierre Mesthe ,  Michel Thomas  and  Marc Vanmeerbeek  as on-site correspondents, and the 102 GPs who carried out the investigation. We also thank  Jeffey Arsham  for the translation and  Yolanda Chen  ( MD, Tennessee USA ) for her English editorial assistance.
| Relieff based features selection Relieff  (Kira & Rendell, 1992; Kononenko, Šimec, & Robnik-Šikonja, 1997 ) is a features selection algorithm that is widely used in the machine learning literature. It estimates a weight for each feature by comparing, for each case, the distance of the closest intra and interclass cases in that feature space and increasing the weight if the distance is greater for the interclass than for the intraclass case. For each modality, we submitted the features surviving the variance threshold to the Relieff algorithm (as implemented in the CORElearn package for R (Robnik-Sikonja & Savicky, 2017)). In order to select the most relevant features we use a scree test approach  (Mori, Hasegawa, Suenaga, & Toriwaki, 2000)  as implemented in  (Meng et al. , 2017). We calculated the selection threshold as the first minimum of the second derivative of the sorted (in decreasing order) and smoothed (via a loess regression  (Cleveland, Grosse, & Shyu, 1992 )) Relieff weights.
| Spatial clustering of the features Features from brain imaging are intrinsically nonindependent. This is partially due to the smoothing applied to the images, but it is above all related to the fact that voxels that lie close usually belong to the same anatomical/functional region. In the light of this, spatially cluster features (i. e. , voxels) that are close to each other is an effective and meaningful way of reducing the number of features. For each modality, we submitted the features surviving the Relieff threshold to a spatial clustering algorithm: contiguous voxels were assigned the same cluster (this step was performed using the spatstat package in R  (Baddeley, Rubak, & Turner, 2015) ). Finally, we extracted the average signal for each cluster, thus effectively reducing the number of features for each modality from hundreds to tens.
| Fitting of the model Finally, the model was fitted using the sequential minimal optimization (SMO) algorithm  (Platt, 1998; Schölkopf & Smola, 2002)  with a polynomial kernel. The model was fitted using the RWeka package  (Hornik, Buchta, & Zeileis, 2009) , a wrapper of the java-based software Weka  (Witten, Ian, Frank, Hall, & Mark, 2011). The free parameters of the SMO algorithm are the order of the polynomial and the lambda (i. e. , allowed error). We left these parameters to the default value of, respectively, 5 and 10.
Rainfall dataset Long-term rainfall series were based on 10-years extension of empirical rainfall event measurements in the Roujan catchment between 1992-2012  (David et al. , 2014 ; Figure  4 ). The total rainfall depth (mm), maximum intensity over 6 min (mm h-1) and rainfall duration (h) were considered for each event, separated by at least a 6-hour dry period. An event with return period >100 years was represented by extreme event measured in Perpignan (120km from Roujan) on 26  October, 1915 (Cosadney and Robison, 2000; Meteo France, 2018). Rainfall depth was 435 mm over 24 hours, but no measurement of rainfall intensity was available. Rainfall intensity was estimated using the Montana law, with results suggest that the Montana coefficient value exceeded the maximum intensity class for the LandSoil model.
Acknowledgement This work benefits from the financial support of  ANR-08-VULN-006  ( LANDSOIL  project) and  MESOEROS21  ( ANR-06-VULN-012 ), ORE OMERE, MISTRALS/Sicmed R_Osmed and MASCC (funded by  ARIMNET2 ). Anna Smetanová received  Marie-Curie   FP7 COFUND People Programme AgreenSkills  fellowship (grant  267196 ). Author thank to  Dr. Mitchell Donovan  for his helpful comments and remarks.
Information geometry and the gamma manifold Information geometry deals with parameterized families of distributions whose parameters are understood as local coordinates on a manifold and provided with a Riemannian structure by the * Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. ucla. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. ucla. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List. pdf Fisher metric. In the sequel, Θ will be a smooth manifold and (p θ ), θ ∈ Θ a family of probability density functions defined on a common event space Ω and depending smoothly on the parameters θ.
Study set-up and design Data used in the preparation of this paper were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. ADNI is a project that has been initiated in 2004 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Food and Drug Administration (FDA), whose principal investigator is Dr. Michael Weiner. ADNI provides all data without embargo to all scientists in the world. The aim of the project is the development of clinical, genetic, biochemical or imaging biomarkers for the early diagnosis and follow-up of Alzheimer's disease. For up-to-date information, see ADNI website.
Participants Our study is based on a part of ADNI population. Indeed, the initial subjects were not age-and sex-matched and our procedure consisted in randomly selecting subjects. In addition, some of the subjects were excluded because of a low diagnosis reliability (according to ADNI criteria) and others because of unsuccessful cortical thickness measurement due to poor image quality. The resulting population is composed of 143 subjects; 71 healthy controls (HC) subjects and 72 Alzheimer's disease (AD) patients whose demographic data are presented in Table  1.
Cortical thickness measurement and distribution Cortical thickness was chosen as the MRI biomarker because of its ability to quantify morphological alterations of the cortical mantle in early stage of AD. Cortical Thickness (CTh) was measured using the Matlab Toolbox CorThiZon  [16]  and computed on the entire cortical ribbon using a Laplace's-equation-based algorithm as described by Jones et al  [17]. Thus, a 3D cortical thickness map was obtained. We applied the method of moments previously described to estimate the three generalized gamma parameters (α, λ, β) and thus we obtained the CTh distribution.
Clustering Based on Distribution Similarity Clustering, also called unsupervised classification, has been extensively studied for years in many fields, such as data mining, pattern recognition, image segmentation and bioinformatics. This technique is used primarily to segment or classify a database or extract knowledge to attempt to identify subsets of data that are difficult to distinguish. The aim is to group data sets in a way that the intra-cluster similarity is maximized while the inter-cluster similarity is minimized. Three principal categories of clustering exist in literature, namely partitioning clustering, hierarchical clustering and density-based clustering. In our study, the experiments were conducted using partitioning k-medoids algorithm  [18] , that we extended using an approximate geodesic distance that is computed in two steps.
Careful and thorough examinations of the DIB parameters, the continuum shape and the quality of the adjustment of the model to the data were done. We compared our DIB measurements with the stellar extinctions A V from the Starhorse database. We then compared the resulting DIB-extinction ratio with the dust optical depth derived from Planck data, globally and also separately for nearby off-Plane cloud complexes. Results. Our analysis has led to the production of a catalog containing 124 064 new measurements of the 15 273 Å DIB, allowing us to revisit the correlation between DIB strength and dust reddening. The new data reveal clearly that the sky-averaged 15 273 Å DIB strength is linearly correlated with A V over two orders as reported by earlier studies but leveling off with respect to extinction for highly reddened lines-of sight behind dense clouds.
(2018). Here we analyze all available red giant stars (160 000) from the public Cannon data products. To do so we used the Can-nonStar file which contain Cannon labels, normalized observed spectra and associated uncertainties as well as synthetic spectra for each single star. Such Cannon results can be found at path 2  /cannon/LOCATION_ID/cannonStar*. fits. The difference between Cannon and ASPCAP has significant consequences on our DIB extraction, as will be detailed in the next section.
Starhorse extinction and distance database In order to study the link between the 15 273 Å DIB strength and the color excess (section 4), we made use of the stellar extinctions in the visible range A V calculated with the Bayesian Starhorse method developed by  Santiago et al. (2016)  and  Queiroz et al. (2018). The authors applied the Starhorse code to APOGEE DR 14 data and the associated stellar parameters derived from the Cannon method, therefore there is a consistency in using the results of the StarHorse technique (here version 1) applied to APOGEE DR14 and the Cannon DR14 model spectra. Starhorse also contains spectrophotometric distances or parallax distances from the Tycho-Gaia Astrometric Solution  (TGAS, Gaia Collaboration et al. 2016)  when available, as described in  Anders et al.
Planck Galactic dust data To trace Galactic dust properties in regions with DIB measurements and allow better comparisons with the interstellar dust along the sightlines, we made use of the Planck Legacy archival 4  data. The Planck Collaboration used Planck and WMAP data to model the spectral energy distributions of the dust thermal emission and derive an all-sky map of the dust optical depth at 353 GHz, τ 353 and of the dust temperature, T D. Here we used the Planck 2015 results described in Planck  Collaboration et al. (2016).
DIB and dust extinction Taking advantage of evolutions and improvements of models and spectra, we have revisited the link between 15 273 Å DIB strength and the extinction. To do so, we cross-matched our catalog of 15 273 Å DIBs (section 3) and the Starhorse Cannon database using TOPCAT  (Taylor 2005). We removed stars that are flagged with NUMMODELS_BAD, EXTINCTION_BAD and BRIGHT2MASS_WARN in Starhorse catalog. We finally obtained a number of 124 064 stars with DIB detection and photometric extinction and distance. Figure  2  shows the equivalent width of the DIB as a function of the extinction along the sightline to the target. As expected and firstly discovered by  Zasowski et al. (2015)  for this DIB, there is a strong correlation between these two quantities that extends over more than two orders of magnitude.
e. a quasi-perfect mono-cloud situation in which all the extinction is generated in a unique dense cloud. To do so, we have added to the figure the results obtained in  Elyajouri et al. (2017a)  for the Barnard 68 dark cloud. The corresponding DIB EW data points correspond to some of the largest departures from the values predicted by the linear relation. However, they are still comparable to tens of other similar cases, suggesting that the marked disappearance of DIBs in Barnard 68 is far from an exception. Article number, page 8 of 16.
3 kpc for Cepheus. The same striking association between cloud cores and low DIB-extinction ratios is immediately visible. A visual inspection of the three figures does not reveal any marked difference between the three cloud associations. In order to quantify better the DIB level off we have represented in Fig. 10  log(EW/A V ) as a function of log(τ 353 ) for the three regions and the same selected targets located beyond the clouds. A first attempt to quantify the level off was made by means of a linear fit in the log-log scale for the merged datasets. The resulting best fit relationship between EW/A V and τ 353 was found to be: EW/A V = 7. 6 × 10 -5 × τ -0. 66±0. 01 353 (Åmag -1 ) (7) We also performed the same adjustments for the three regions separately, this time taking into account uncertainties on the DIB to EW ratio (the fractional error on the ratio is estimated as the square root of the quadratic sum of fractional errors on the two terms).