dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The WD methodology is presented in Sec. 3. It is applied here to three climate fields: • Near-surface air temperature; • Precipitation; and • Sea ice cover, computed from the sea ice area fraction. The corresponding daily mean fields are available in the CMIP5 simulations for historical and RCP85 forcings  (Taylor et al. , 2012)  and they are ranked with respect to the distance from reference daily datasets, specifically European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA) Interim for the temperature  (Dee et al. , 2011) ; Global Precipitation Climatology Project (GPCP) for the precipitation  (Adler et al. , 2003) ; and Ocean and Sea Ice -Satellite Application Facility (OSI-SAF) for the sea ice cover (EUMETSAT Ocean and Sea Ice Satellite Application Facility, 2017). In order to further support the comparison and provide a benchmark, we analyzed the WD with respect to the National Center of Environmental Prediction (NCEP) Reanalysis 2  (Kanamitsu et al.
, 2002). The acronyms of the models considered here are given in Table  S1  of Text S2 in the SI. The samples used in the WD calculations are drawn by performing a  Ulam (1964)  discretization of the phase space involved in each separate test. To do so, a regular grid is superposed over all the datasets used in the test and its upper and lower limits, respectively, are fixed slightly above and below the maximum and minimum values among all the datasets used in it. Each dimension of the grid is then equally divided into 20 intervals; this yields 20 m m-dimensional cubes, where m is the number of fields taken into account in the test. These 20 m hypercubes provide the sample for each test. The results we present here are weakly sensitive to the specifics of the gridding. Nonetheless, a too coarse gridding removes a lot of the information we want to retain and analyse; a too fine gridding, instead, increases substantially the computing requirements, without making much statistical sense.
Wasserstein distance Our objective is to create a ranking of the CMIP5 IPCC models based on their skill to reproduce the statistical properties of selected physical quantities. The reference dis-tribution for these quantities is given by reanalysis and observational datasets, as explained in Sec. 2; their WD to these datasets is a measure of the models' ability to reproduce these reference distributions. One can also describe this distance as the minimum "effort" to morph one distribution into the other  (Monge, 1781). We present below a very simplified account of the theory. The optimal transport cost  (Villani, 2009)  is defined as the minimum cost to move the set of n points from one distribution to another into an m-dimensional phase space. In the case of two discrete distributions, we write their measures µ and ν as µ = n i=1 µ i δ xi , ν = n i=1 ν i δ yi ; (1) here δ xi and δ yi are Dirac measures associated with a pair of points (x i , y i ), whose fractional mass is (µ i , ν i ), respectively, and n i=1 µ i = n j=1 ν j = 1 , where all the terms in the sum are nonnegative.
, k x m. We then define d(x, y) 2 = m l=1 (k x l -k y l ) 2. To further simplify the computations, we exclude all the grid boxes containing no points at all. Finally, we divide the distance by n; therefore, the one-, two-and three-dimensional WDs take values between a minimum of 0 and a maximum equal to 1, √ 2 and √ 3, respectively. We used a suitably modified version of the Matlab software written by G. Peyré -available at https://nbviewer. jupyter. org/github/gpeyre/numerical-tours/ blob/master/matlab/optimaltransp 1 linprog. ipynb -to perform the calculations. The modifications include the data binning and the estimation of the measures, as well as adapting to a dimension m ≥ 2.
Ranking the models Figure  1  shows the WD calculated in the two-dimensional phase space composed by the temperature and precipitation fields, averaged over the whole Earth and the Tropics, for each CMIP5 model. In order to provide a benchmark, we chose to include the WD results between the NCEP reanalysis and the references given by the ERA temperature and GPCP precipitation fields, respectively. Somewhat surprisingly, the NCEP reanalysis yields the largest values in both distances. Thus, the average CMIP5 distance to the combined ERA-and-GPCP reference datasets is 0. 149, while the NCEP distance is 0. 259, exceeded only by the value 0. 264 given by the MIROC5 model; see Table  S1  in the SI for the list of models. Note that the one-dimensional WDs of the NCEP Reanalysis for the globally averaged temperature and precipitation equal 0.
033 and 0. 255, respectively, which indicates the inadequacy of the NCEP dataset in representing the statistics of precipitation. Despite the well-known difficulties with simulating the very rough precipitation field by using the still fairly coarse CMIP5 models  (Neelin et al. , 2013; Mehran et al. , 2014) , the results point to the overall accuracy reached by CMIP5 simulations when dealing with global averages of temperatures and precipitation. We evaluate next the problems still encountered by CMIP5 models in reproducing key aspects of tropical dynamics  (Tian & Dong, 2020). Averaging the data over the Tropics, we obtain the ranking on the vertical axis in Fig. 1. The WD distance is for most datasets larger than when looking at globally averaged quantities (the models' mean is uniformly so: while in Fig.
Conclusions We have proposed a new methodology to study the performance of climate models based on the computation of the Wasserstein distance (WD) between the multidimensional distributions of suitably chosen climatic fields of reference datasets and those of the models of interest. This method takes into account all the moments of the distributions, unlike most evaluation methods for climate models used so far, which consider solely the first two moments of the distribution. It is, therefore, more informative and takes into account also the distribution of extreme events. The methodology allows one to consider several variables at the same time, and it helps select such variables depending on the goal of the intercomparison. Thus, it can assist in disentangling the correlation between different climatic quantities.
The fields are averaged on four distinct domains: (i) Global; (ii) Region between 30 S and 30 N (Tropics); (iii) Region between 30 N and 90 N (Northern extratropics); and (iv) Arctic -used only for sea ice extent. While temperature and precipitation analyses involve 30 models, taking into account sea ice extent allows to analyze just 22 models, due data availability. datasets. The time range spans 18 years, from 01/01/1997 to 12/31/2014. After the spatial averaging, the model datasets are obtained by concatenating the historical runs, from 1997 to 2005, and the RCP85 runs, from 2006 to 2014.
Acknowledgments GPCP and NCEP Reanalysis 2 data have been provided by  NOAA/OAR/ESRL PSD  at https://www. esrl. noaa. gov/psd/. The EUMETSAT OSI-SAF 1979-2015 sea ice concentration (v2. 0, 2017), has been provided by  ICDC, University of Hamburg , at https://.
It has been observed that these online resources have been used by a broader community of teachers in many fields, including natural sciences, history, geography, social sciences and so on. Through this unique, long-term experience, this paper contributes to answering the following questions. How and why has the French educational system evolved over time and benefited from the deployment of a dedicated seismic network? Why is collecting scientific data inside a school important for both teachers and students? How have motivated teachers been able to elaborate online educational exercises to study their own data as well as the data that has become increasingly available on the web? How have such original educational resources been shared with other teachers across school and national boundaries? What skills and supports are needed to maintain an active educational network? Why can we not rely only on the open datasets available on the web? How has such an experience impacted French teaching programs?.
Students and teachers tracked seismic events, such as the Chi Chi earthquake (Fig. 1b ), as well as other natural or anthropogenic vibrations (swell, human activity, quarry blasts, etc. ). In 1996, the French Ministry of Education decided to connect all the schools to the Internet for student training. However, these institutions were very concerned about the added pedagogical value of the Internet in schools. The seismic network thus benefited from this evolution in connectivity: the prototype seismometer was connected to the Internet network. The first online educational seismic database was born. Teachers in other schools were able to work on the online seismic datasets. At the same time, regional political concerns focused on the education of young people about natural hazard awareness in one of the most active seismic zones in France.
Regional, national, and international teachers' workshops In the last 25 years, different meetings have been proposed to any teacher who wants to increase their own skills in seismological topics. In order to maintain significant momentum within the national (and overseas) network, six national meetings have been held since the beginning of the project, with a total of 200 teachers attending at least one of the proposed meetings. These ongoing training sessions thus keep the educational network alive. Topics are related to following items: -scientific conferences presented by scientists and specialized teachers, -practical workshops around datasets from the network, -poster presentations to share experiences at different schools. This format was finally recognized as very useful and helpful for the optimal use of scientific datasets, through the interaction of researchers participating in these teachers' workshops and with the participation of the young students whose awareness has been raised.
A long-standing production of teaching resources At the early stage of the network, the teachers shared their pedagogical experiences of exploiting in-school seismometers and available datasets on the web. Initial activities focused on the interpretation of dates, travel times and picks of seismic waves. With the accumulation of new data combined with their increasing seismological skill, teachers started to produce better-developed activities focusing on different and more complex aspects. The development of digital tools at school has enabled the development of original activities to manipulate numerical quantities. For example, by combining information from seismic catalogs and spreadsheet tools, students are able to display coordinates of listed seismic events on a map and observe that the distribution of each plot enables them to highlight areas which look like tectonic plates.
Successive web platforms to improve sharing All records and activities have also centralized on successive dedicated web platforms for open sharing. At the origin of the project, a web server was hosted by the regional services of the French Ministry of Education. Thus, the records from regional and global seismic activity feed an online database and constitute a seismic resource center for education. Teaching requires a didactic approach to resources. The need for a more sophisticated platform emerged: this is why the web interface of our educational program goes beyond a simple data center. In 2010, the.
New impetus for natural risk prevention with the EduMed Observatory Since 2017, University Côte d'Azur has taken over with the program called Educational Mediterranean Observatory ("EduMed-Obs", http://edumed. unice. fr). EduMed-Obs focuses on implementing an interface based on a geoscience dataset concerning the Mediterranean basin. The theme not only focuses on seismology: landslides, meteorology, hydrology, and sea-level variations are also considered. Data mining is developing and become preponderant in current teaching programs  (Bigot-Cormier et al. , 2017). These datasets are intended not only for middle and high schools, but also for university students. EduMed-Obs also provides datasets from research centers. This aspect is important to strengthen the visibility of the activities of research institutes.
It is an excellent opportunity for students to compare datasets from their own sensors with research datasets. Making populations aware, through student training, of the role of earth science observatories is crucial and promotes a better understanding of the seismological (and environmental) nature of the territory where students live. How these observatories participate in our seismic risk awareness is better understood by political structures, inducing improved territorial management, such as tsunami mitigation. This new educational observatory already numbers some seventy European schools in the countries around the Mediterranean that host sensors and which implement scientific teaching focused on natural risk education (schools map available at http://edumed. unice. fr/fr/le-reseau-edumed).
A look at the past actions of the French educational seismological network Teachers have reported a number of positive points from their experience: students' enthusiasm for recording quakes, the ease of understanding online databases, the development of autonomy, students' responsibility in managing a seismological station, the importance of natural risk within the theme of sustainable development. One of the great strengths of the network has been its integration into teaching programs, and to fulfill various expected educational objectives: -practice a scientific approach; -demonstrate observation skills, curiosity, critical thinking; -experience autonomy; -communicate in scientifically appropriate language: oral, written, graphical, numerical. The installation of seismometers in schools in different areas of Europe and abroad has given the necessary impulse to use a scientific approach for the improved development of activities concerning the knowledge of hazard, the real-time manipulation of information and scientific databases, as well as a better understanding of matters related to risk and territorial management  (Courboulex et al.
https://doi. org/10. 5194/gc-2020-32 Preprint. Discussion started: 20 August 2020 c Author(s) 2020. CC BY 4. 0 License. "www. edusismo. org" website was developed through funds provided by the French ministerial "Sciences à l'École" program. This website was a cornerstone to provide tools (experiments, software, and simulation) to properly exploit the available datasets as well as many different educational paths. The two first main digital tools used were: -SeisGram2K (Lomax A. , 2000, see "Data and Resources") software, an interface for the seismological research community and adapted for schools. Students can display seismograms, apply filtering processes, pick arrival times, and more; -EduCarte geographical information software (see "Data and Resources") which enables users to plot geo-referenced information, work with seismograms, display GPS measurements, create cross sections, and more.
Acknowledgments We are especially grateful to  Guust Nolet  and the reviewers for the remarks and suggestions that helped us to greatly improve this paper. The educational network and activities presented in this paper have been supported from the beginning by the  French Ministry of Education. It has also been supported and funded by the  French Ministerial  " Sciences à l'École " project, the  European NERA (Network of European Research Infrastructures for Earthquake Risk Assessment and Mitigation)  and  SERA (Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe , Grant Agreement Number  730900 ) research projects, the  French Géoazur laboratory , and the  Université Côte d'Azur  through the EduMed Observatory since 2018 ( UCA-JEDI Investments in the Future  project under reference number  ANR-15-IDEX-01 ).
Deep Convolutional Neural networks Generally, three datasets are introduced in a neural network architecture: a training dataset, made to learn the DCN with expert annotations of the elements of concern in a part of the input image; a test dataset made to quantify the ability of the trained DCN (generalization ability) to predict the elements of concern in another unseen part of the image; and a previously unseen validation dataset made to both check the generalization performance as training is progressing, and to stop the training before we over-train the DCN ("overfitting"); overfitting indeed happens when the network is trained so much on a set of images that it can only reproduces that images, with high accuracy. As the computational cost to feed a large size image is very high, the training is done on smaller sub-images extracted from the source image.
GAN versus conditional GAN Let's assume we have a dataset with N training images xn and the corresponding ground truth label yn, with probability distributions P datax and P datay , respectively. Then, the conventional loss function  (Goodfellow et al. , 2014)  of the GAN network is defined as follows: min D max G LGAN (D, G) = Ex∼P datax [logD(x)] + Ez∼P z [log(1 -D G(z) ] (2) where z is an input noise variable drawn from a probability distribution Pz. Conditional version of GAN can be constructed by feeding extra information yn to both the Generator and Discriminator networks, instead of noise variable. One can define the following loss function for the Conditional GAN (CGAN): min D max G LcGAN (D, G) = Ex∼P datax ,P datay [logD (x, y) ] + Ex∼P datax [log(1 -D (x, G(x)) ] (3) The first term in the above equation maximizes the probability to correctly predict the output variable y.
EXPERIMENTAL STUDIES Dataset: We use a georeferenced optical four band image with spatial resolution 5 × 10 -4 m and size 20164 × 6807, constructed with photogrammetry from hundreds of hand-held camera pictures taken on the field (Figure  3(a) ). Here, we use only the red (R), green (G) and blue (B) bands of the image. The image encompasses faults and fractures cutting a granite rock. The distorted or incomplete parts of the image at the site corners were not used. The entire image is divided in three regions, based on the geographical location, for training, validation and test, including 384, 94, 222 sub-images, respectively. This type of division which generates test sub-images that are far from the training ones is also recommended by  Jafrasteh et al. , 2018; Bastani et al. , 2018. Figure 3(c)-3(d)  shows one of the patches in the image.
More work still needs to be done however, especially to include more fault classes reproducing their natural hierarchy (major faults, more minor faults, faults uncertain at different degrees, etc. ), and to take into account that manual fault maps are inherently incomplete; this might bias the performance criteria analysis. Furthermore, most faults form a topographic imprint, and therefore, integrating topography in the input data would be useful. Eventually, we will need to apply our new method to different datasets (different image types and resolutions, including satellite and aerial images), so as to verify that our novel approach can be generalized.
The study of multi-decadal hydrological variability requires long instrumental records (>100 years), but such long-time series are non-existent for the Labrador region. Recently, rainfall-runoff modeling approaches have been used to expand instrumental streamflow datasets, using long-term climatic reanalysis as inputs. Rainfall-runoff modeling was used by  Brigode et al. (2016)  to reconstructed daily streamflow series over the 1881-2011 period in northern Québec. Nevertheless, this type of methods suffers from the limited observations in order to evaluate and validate the reconstructed hydro-climatic temporal series. The deficiency of observations led to the exploration of various natural archives for reconstructing past hydro-climatic conditions. Long hydro-climatic series based on natural proxies in the study region are rare and limited to tree-ring  (Dinnis et al.
, 2019; Boucher et al. , 2017; Begin et al. , 2015; Naulier et al. , 2015; Naulier et al. , 2014; Nicault et al. , 2014; Boucher et al. , 2011; Begin et al. , 2007; D'Arrigo et al. , 2003)  and pollen datasets  (Viau et al. , 2009). In this perspective, clastic varves formed and preserved in river-fed lakes have the potential to produce long paleohydrological series. Clastic varves can provide, in favourable settings, annually to seasonally resolved information about downstream sediment transport from the catchment area the into lake basin depending on regional https://doi. org/10. 5194/cp-2020-87 Preprint. Discussion started: 20 July 2020 c Author(s) 2020. CC BY 4. 0 License. hydro-climatic conditions  (Lamoureux, 2000; Lamoureux et al. , 2006; Tomkins et al. , 2010; Cuven et al. , 2011; Kaufman et al.
Sediment coring and processing Four short sediment cores (BEA-1, NAS-1A, NAS-1B and NAS-2) were collected using a UWITEC percussion corer in March 2017. These cores were collected in undisturbed area according to the swath bathymetry and subbottom profiling data  (Trottier et al. , 2020)  in the axis of the Beaver (BEA-1) and Naskaupi (NAS-1, NAS-2) river mouth at a depth of 93, 146 and 176 m respectively. Site NAS-1 is located at the distal frontal slope of the Naskaupi River delta slope; site NAS-2 is located away from the delta, at the beginning of the deep lake basin. Efforts were made to retrieve the cores without disturbing the sediment water interface. Duplicate cores have been retrieved at each site to maximize the sediment recovery. The cores were scanned using a Siemens SOMATOM Definition AS+ 128 medical CT-Scanner at the multidisciplinary laboratory of CT-scan for non-medical use of the Institut National de la Recherche Scientifique -Eau Terre Environnement (INRS-ETE).
The CT-scan images allowed the identification of sedimentary structures (i. e. , laminated facies, perturbation and hiatus). Expressed as CT-numbers or Hounsfield units (HU), X-Ray attenuation is function of density and the effective atomic number, and hence sensitive to contrasts in mineralogy, grain size and sediment porosity  (St-Onge et al. , 2007). CT- numbers were extracted at a resolution of 0. 06 cm using the ImageJ software 2. 0. 0 (imagej. net). The cores were then opened, described and photographed with a highresolution line-scan camera mounted on an ITRAX core scanner (Cox Analytical Systems, Sweden) (RGB color images; 50 µm-pixel size). Geochemical non-destructive X-Ray Fluorescence (XRF) analysis was performed on the core half (30 kV and 30 mA) at the INRS-ETE. XRF elements profiles were used to visualize varves and their sub-layer boundaries, micro-facies and estimate particle-size variability in sediment cores  (Kylander et al.
Chronology and thickness measurement Surface sediments from cores BEA-1 and NAS-1A were dated with 137 Cs method  (Appleby and Oldfield 1978)  using a high-resolution germanium diode gamma detector and multichannel analyzer gamma counter. A sampling interval of 2 cm to ±0. 5 cm was used in order to sample each lamination for the 1961-1965 period. 137 Cs activity was used to identify sediment deposited during 1963-1964 peak of nuclear tests and validate the annual character of the layers. In order to establish a chronology for each core, detailed laminations counts were executed repeatedly on CT-scan images and high-resolution photographs using ImageJ 2. 0. 0 and Adobe Illustrator CC softwares. As all of the core surface has been well preserved, the first complete lamination below the sediment surface was considered to represent the topmost year (i.