dataesr/scientific-paragraphs-categorization huggingface dataset:
The microsatellite set and subspecies sample are indicated at the end of each statistics by indices k_i for single population statistics and k_i. j for two population statistics, with k=1 for the set of untranscribed microsatellites or k=2 for the set of transcribed microsatellites, and i(j)=1 for the S. g. flaviventris subspecies or and i(j)=2 for the S. g. gregaria subspecies. See Table  1  for details on the summary statistics abbreviations. Five noise variables, randomly drawn into uniform distributions bounded between 0 and 1, and denoted NOISE1 to NOISE5 were added to the set of summary statistics processed by RF, in order to evaluate from which amount of decrease in the Gini criterion the summary statistics computed from our genetic datasets were not informative anymore (indicated by a red star).
The contribution of each 32 summary statistics is evaluated as the total amount of decrease of the residual sum of squares, divided by the number of trees, (variable importance on the x-axis). The higher the contribution of the statistics, the more informative it is in the inferential process. The microsatellite set and subspecies sample are indicated at the end of each statistics by indices k_i for single population statistics and k_i. j for two population statistics, with k=1 for the set of untranscribed microsatellites or k=2 for the set of transcribed microsatellites, and i(j)=1 for the S. g. flaviventris subspecies or and i(j)=2 for the S. g. gregaria subspecies. See Table  S6. 1 for details on the summary statistics abbreviations. Five noise variables, randomly drawn into uniform distributions bounded between 0 and 1, and denoted NOISE1 to NOISE5 were added to the set of summary statistics processed by RF, in order to evaluate from which amount of decrease in the variable importance criterion the summary statistics computed from our genetic datasets were not informative anymore (indicated by a red star).
2008) ; and MAXENT, using the library dismo and all default parameters  (Elith et al. 2011 ). As mentioned above, background data was sampled at random in Africa from outside the species current distribution. Pseudo-absences received a combined weight of 50% when using GAM or BRT, in order to balance the large number of pseudo-absences with respect to presence records in the modelling process while representing the range of values of the environmental predictors in the study area  (Barbet-Massin et al. 2012). The consensus was drawn as the median predicted probability between the three models, and four different threshold values were calculated from this consensus: the MST threshold (the threshold that maximizes the sum of sensitivity + specificity), often recommended as a good strategy to optimize presence and absence classification rates  (Liu et al.
Classification success rates (AUC, TSS, sensitivity and specificity) were calculated for the consensus on all the dataset, using an MST threshold when needed. The thresholds calculated using current climate and occurrence data were then projected into past scenarios (HCO and LGM) using 3 different global circulation models at the same resolution: CCSM4, MIROC-ESM and MPI-ESM-P. We chose these three GCMs because their projections were available for both time steps of interest. Although the three GCMs are based on climatological principles, each one works in slightly different ways, and therefore produces different results. Consensus between GCMs presented below represents median values of predictions between the three GCMs.
Such posterior probability provides a confidence measure of the previous prediction at the point of interest η(y). It relies on the building of a regression random forest designed to explain the model prediction error. More specifically, and as a first step, posterior probability computation makes use of out-of-bag predictions of the training dataset. Because each tree of the random forest is built on a bootstrap sampling of the H elements of the reference table (i. e. the training dataset), there is about one third of the reference table that remains unused per tree, and this ensemble of left aside datasets corresponds to the out-of-bag. Thus, for each pseudo-data of the reference table, one can obtain an out-of-bag prediction by aggregating all the classification trees in which the pseudo-data was out-of-bag.
Local posterior errors In the present paper, we propose some posterior versions of errors, which target the quality of prediction with respect to the posterior distribution. As such errors take the observed dataset η(y) into account, we mention them as local posterior errors. For model choice, the posterior probability provided by Algorithm 3 is a confidence measure of the selected scenario given the observation. Therefore  Simulated datasets (5,000 par divergence time) were generated for fixed divergence time values of 100 ; 250 ; 500; 1,000 ; 2,500; 5,000 ; 10,000 ; 25,000 ; 50,000; 100,000; and 250,000 generations. The median (plain lines) and 90% credibility interval (90% CI; dashed lines), averaged over the 5,000 datasets, are represented. Divergence time values are in number of generations.
This suggests an overall low power to discriminate among scenarios considered. Conversely, considering pairwise groups of scenarios, one can observe a weaker overlap of projected points (at least for a) and b)) suggesting a stronger power to discriminate among groups of scenarios of interest than when considering all scenarios separately. One can note that the location of the observed dataset (indicated by a vertical line) suggests an association with the scenario group with a bottleneck event in S. g. flaviventris and with the scenario group with a population size contraction in the ancestral population.
Five noise variables, randomly drawn into uniform distributions bounded between 0 and 1, and denoted NOISE1 to NOISE5 were added to the set of summary statistics processed by RF, in order to evaluate from which amount of decrease in the Gini criterion the summary statistics computed from our genetic datasets were not informative anymore (indicated by a red star). a) B = demographic bottleneck event, b) C = demographic contraction event and c) AU or AB or no A = unidirectional from S. g. gregaria into S. g. flaviventris or bidirectional genetic admixture event.
Acknowledgements This work was supported by research funds from the  French Agricultural Research Centre for International Development (CIRAD) ,  the  project  ANR-16-CE02-0015-01  (SWING), the  INRA scientific department SPE  ( AAP-SPE 2016 ), and the  Labex NUMEV (NUMEV ,  ANR10-LABX-20 ). The data used in this work were partly produced through the technical facilities of the  Centre Méditerranéen Environnement Biodiversité, Montpellier. We are grateful to  Pierre-Emmanuel Gay  for assistance with maps, and  Antoine Foucart ,  Gauthier Dobigny  and  Jean-Yves Rasplus  for fruitful discussions. We are extremely grateful to the  Peer Community In Evolutionary Biology recommenders  ( Concetta Burgarella  and  Takeshi Kawakami ) and reviewers ( Michael D Greenfield  and two anonymous ones) for their wise and helpful comments on two earlier versions of the manuscript.
flaviventris subspecies or and i(j)=2 for the S. g. gregaria subspecies. See TableS6. 1 for details on the summary statistics abbreviations. Five noise variables, randomly drawn into uniform distributions bounded between 0 and 1, and denoted NOISE1 to NOISE5 were added to the set of summary statistics processed by RF, in order to evaluate from which amount of decrease in the variable importance criterion the summary statistics computed from our genetic datasets were not informative anymore (indicated by a red star).
Summary statistics provided by DIYABC with corresponding values computed from the observed microsatellite dataset. Summary statistics Observed values at 14 untranscribed markers Observed values at 9 transcribed markers NAL 28. 8 15. 8 S. g. gregaria one-sample statistics HET VAR 0. 92 36. 1 0. 79 13. 3 MGW 0. 92 0. 86 NAL 23. 4 14. 4 S. g. flaviventris one-sample statistics HET VAR 0. 86 33. 4 0. 69 16. 7 MGW 0. 96 0. 95 FST 0. 04 0. 12 Two-samples DAS 0. 07 0. 16 statistics LIK Sgg  Sgf 3. 61 2. 82 LIK Sgf  Sgg 3. 20 2. 55 DM2 22. 7 12. 4 N2P 35. 0 21. 1 H2P 0. 91 0. 79 V2P 40. 2 18. 2.
The number of records in the reference datasets (n ref ) simulated from DIYABC varied from 50,000 to 100,000. The number of RF-trees was set to 2,000. (a) Replicate analyses have been processed on ten reference tables. (b). The normalized mean absolute error (NMAE) is the absolute difference between the point estimate (here the median) and the (true) simulated value divided by the (true) simulated value.
2017). With LongRunMIP, we aim to advance knowledge in the above mentioned areas, fill a gap in the CMIP protocols  (Taylor et al. 2012; Eyring et al. 2016) , and collect published data in one location for easy public access. The goals of LongRunMIP are as follows: 1) to continuously gather existing millennial-length simulations (both published and unpublished), 2) to standardize the collected data (e. g. , using the same units and sign conventions), 3) to make the data publicly available and easily accessible, and 4) to foster an interdisciplinary community of users working on millennial-length problems, with experts on oceanography, atmospheric dynamics, energy balance modeling, ice sheet modeling, and paleoclimatology. The objectives of this paper are as follows: 1) to motivate the data collection strategy, 2) to specify the requirements for LongRunMIP contributors, 3) to give an overview of currently submitted simulations and models, 4) to give a sample of some initial analysis on these simulations, 5) to show how LongRunMIP relates to the existing literature on millennial-length simulations, and 6) to discuss the limitations and opportunities of LongRunMIP.
The set of variables we collect is motivated by the interest of the LongRunMIP contributors and organizers in ECS, temperature-and time-dependent feedbacks, and deep ocean warming. Table  1  lists the variable names, units, and temporal and spatial resolution of the requested variables. The naming and sign conventions follow the CMIP5 protocol. 1 Given the large amount of data involved, we have kept the list of requested variables short to allow as many groups as possible to participate. For the same reason, we do not request the data to be "CMORized," 2  (i. e. , written in conformance with the CMIP standards). However, we do homogenize signs, variable long names, and units, and also provide a regridded version of the fields, as well as global means. Minimal, optimal, and current contributions.
LongRunMIP builds upon a body of pioneering studies that looked at the behavior of models beyond the centennial scale (Table  3 ), LongRunMIP allows this sort of analysis to be applied across a diverse group of models that exhibit strikingly different behavior (Fig. 7 ), and hopefully encourages others to look beyond the limitations and assumptions normally imposed by computational constraints, to directly study the equilibration of the fully coupled atmosphere-ocean system. Data access and sharing. LongRunMIP currently consists of 15 TB of data and are available for download (https://data. iac. ethz. ch/longrunmip/). Fields shown in this paper can be accessed online (https://data. iac. ethz. ch/longrunmip/BAMS/). See www. longrunmip. org for more details on available variables, contact information, sample figures and videos (see supplemental material), and links to join a discussion community.
ACKNOWLEDGMENTS. MR acknowledges support from the  Swiss National Science Foundation  under Grant  P2EZP2_175097  and is funded by the  Alexander von Humboldt Foundation. TA was supported by the  Joint U. K. BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). NCAR is a major facility sponsored by the  U. S. National Science Foundation  under Cooperative Agreement 1852977. TLF acknowledges support from the  Swiss National Science Foundation  under Grant  PP00P2_170687 , from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement 821003 (CCiCC), and from the  Swiss National Supercomputing Centre (CSCS). CL was supported through the  Clusters of Excellence CliSAP  ( EXC177 ) and CLICCS ( EXC2037 ),  University Hamburg , funded through the  German Research Foundation (DFG).
SY was partly supported by  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/20072013 )/ ERC  Grant Agreement 610055 as part of the ice2ice project. This work was made possible for IPSL thanks to the HPC resources of TGCC and IDRIS made available by  GENCI  ( Grand Equipement National de Calcul Intensif ),  CEA  ( Commissariat à l'Energie Atomique et aux Energies Alternatives ), and  CNRS  ( Centre National de la Recherche Scientifique ) (Project  016178 ).
The two latter categories (partially and completely consumed) were grouped within the status 'consumed'. Respectively, the visiting fish behaviors observed within the experimental setup (during the 90 minutes) were classified into four categories: (1) present (i. e. species present around the experimental setup without showing specific interest in the tethered fishes), (  2 ) interested (i. e. species being specifically interested in one or more tethered fish), (3) partial consumer (i. e. species biting one or more tethered fish), (  4 ) consumer (i. e. species eating completely a tethered fish). Examples of each visiting fish behavior type are provided in Supplementary Video S1. Overall, 54 hours of video footage were recorded and analyzed. To compare the consumption rate of tethered specimens (using the data either from in situ visual observations or from the video recordings) among locations and between protection levels, we used Generalized Linear Models for Multivariate Binomial Data  (Wang et al.
2012). Terms included in the models were the factor 'protection' (fixed, 2 levels: NTZ vs OUT), the factor 'location' (fixed, 3 levels: CAB, KAS, TAV) and the interaction term (twoway crossed design). The factor 'location' was fixed to allow for the comparison of patterns among the three locations distributed along a longitudinal gradient. Significance of terms was assessed using analyses of deviance. P-values were calculated using 999 resampling iterations via PIT-trap resampling (to account for correlation in testing). Models and tests were carried out using the mvabund R package  (Wang et al. 2018). Binomial confidence intervals of probabilities (of tethered fish being consumed) were calculated using Bayesian inference  (Gelman et al. 1997)  with the binom R package (Dorai-Raj 2014).
Predator arrival time and interaction with tethered fish When collecting data from the video footages, we also recorded the exact time of the first presence (arrival time) of a species within the experimental setup (i. e. within the camera field of view), and the time of the first predator-tethered fish interaction (i. e. when a visiting fish was interested, or a partial consumer or a consumer). For these two metrics (time of arrival and time of interaction), we produced accumulation curves and boxplots of the number of species within 90 mins from the deployment of the experimental unit. To compare the arrival time and the time of predator-tethered fish interaction between protection levels and among locations, we performed univariate and multivariate Permutational Multivariate Analysis of Variance (PERMANOVA).
Acknowledgements This work is a contribution of the project " PAVIS: Assessing the relationships between marine protected areas and invasive species " funded by the  French National Agency for Research  (Project No. ANR-16-ACHN-0016-01 ). We thank all the MPA authorities that granted permission to conduct fieldwork within their jurisdiction. Special thanks for operational and field assistance to  V. Gerovasileiou, M. F. Huseyinoglu ,  Y. Arda ,  M. Draman ,  E. Desidera ,  A. Cuardos , and  J. A. Garcia Charton. We also thank  R. Stantz  for inspiring the tethering experiment performed in this study.
They demonstrated that dietary composition and richness were similar between size-dimorphic sexes, and that females had greater dietary richness per faeces than males. Accurate diet information is increasingly important in studies assessing the functional role of species, and the metabarcoding approach can allow obtaining high-quality diet information overcoming several limitations of traditional approaches. For instance, metabarcoding can allow species identification even in absence of diagnostic body parts and, if reference databases are available, can identify species with very high taxonomic resolution. A broadest application of this approach has the potential to boost our understanding of several aspects of herps biology, from the reconstruction of food webs to evolutionary and conservation issues.
Physical measurements The elemental analyses for C, H and N were performed in a PE240C elemental analyzer. The infrared spectra were recorded on a Bruker Tensor 27 spectrometer with ATR mode. Thermal analyses were performed in nitrogen with a heating rate of 5 C min À1 on a TGA-DTA V1. 1b Inst 2100 instrument. The powder XRD patterns were recorded on a Shimadzu XD-3A X-ray diffractometer. Magnetization were measured using a Quantum Design SQUID VSM magnetometer. The magnetization data were corrected for the diamagnetic contributions of both the sample holder and the compound obtained from Pascal's constants. 19.
3 ). While for Ni-10H 2 O, the colour changes from a light green, yellow to brown. On rehydrating the colour of the powders reverts back but with a little bit less intensity. The crystallinity of the products of the thermal degradation was checked by powder X-ray diffraction because all attempts using selected crystals indicate they are heavily fragmented with very wide mosaic that hampered structural determination. The PXRD of the partially and fully hydrated phases show reasonable crystallinity and major changes to the unit cells from the virgin crystals (Fig. 4 ). However, the quality of the data remains low and has prevented further structural analyses. Interestingly, upon rehydration in water vapor at 70 C for 3 days the original hydrated forms are reproduced, as conrmed by PXRD and IR measurements (Fig.
Introduction The Pas-de-Calais Department (France) manages 117 secondary school buildings. To improve knowledge of their energy impact, a several-year instrumentation plan was implemented to track both consumption (water, electricity, gas) and temperature  (indoor, outdoor)  in practically real time. A preliminary work on this dataset demonstrated this measurement potential by means of detecting water leaks using a single sensor. It is now being proposed to extend this effort to the application of other measurements, which could quickly become cumbersome and complicated, particularly when considering the dependence on both uses and the environment. To analyze such measurements, we have applied several data mining techniques compatible with such multiple-sensor configurations. These data-oriented techniques have already been employed on a frequent basis in order to support and improve upon the fundamental aspects of energy efficiency management.
The restart is triggered at 5 am and the setting is lowered at 7 pm. This usage breakdown led to separating every day into 3 periods, as follows: • Nighttime: from 7 pm the prior evening until 5 am in the morning (not including reactivation of the heating system); • Morning: from 5 am to 12 pm; • Afternoon: from 12 pm to 7 pm. This breakdown was established after consultation with the facility manager and by taking into account the heating schedule and the weekly usage profile of the secondary school for instruction-based activities. The data measured with a 10-minute acquisition step were aggregated into consumption levels for each period, yielding 9 datasets per day, i. e. 3 for water consumption, 3 for electricity and 3 for gas. This set-up led to processing 9 datasets over an entire year, such that each value contained in these datasets represents the cumulative consumption of the measurement conducted during each period of the day.
The K-Means method seeks to optimize a global objective (variance of clusters) and reach a local optimum, while HAC is aimed at finding the best step in every cluster fusion, which is achieved exactly yet winds up with a potentially suboptimal solution. Meanwhile, K-Means offers a greater advantage in grouping large datasets, and its performance improves as the number of clusters increases. Hence, the K-Means algorithm outperforms the HAC algorithm  (Joshi et Kaur 2013). Ultimately, we opted to utilize K-Means, which is intended to provide a grouping into k families containing "like" individuals (in our case, a series of measurements over a given period), e. g. the number "k" of classes must be set ahead of time. This set-up will serve to define specific consumption classes. The procedure implemented in the K-Means method conforms to the following sequence: 1.
To validate this learning phase, we randomly collected 50% of the data for learning (training dataset) and the remaining 50% for testing (test dataset). To verify the quality of our results, we generated the confusion matrix by comparing data classified using the reference dataset that were needed to run the test  (Ruuska et al. , 2018). one SVM (OVO SVM) method will be used herein in order to classify the daily data of year 2017. SVM refer to non-parametric classifiers, in an effort to obtain the optimal separating hyperplane between binary classes by respecting the maximized margin criterion. Our work however entails a multiclass classification case, which necessitates a one-to-one strategy applicable to any binary classifier introduced to solve a multiclass classification problem  (Liu, Bi and Fan, 2017).
Apart from these orthologues, BLAST searches using SpmX only returned hits for the muramidase domain. These hits came from Gram-negative bacterial genomes that span the entire bacterial domain and from viral genomes. Most of these bacterial genes are likely to be in prophage regions, as evidenced by their position in tracts of prophage genes. We did not detect sequences homologous to SpmX TMs in our search, although we occasionally detected homologous phage muramidase domains fused to other, non-homologous TM segments. Consistent with finding close SpmX muramidase relatives in prophages, NCBI's Conserved Domain Database (CDD) tool  [21, 22]  clustered SpmX muramidase with glycoside hydrolase 24 (GH24) lysozymes in the autolysin/endolysin class. The sequence cluster diagram in Figure  S2  illustrates the inferred, ancient evolutionary relationships between lysozyme families based on sequence and structural alignments.
Method Details Bioinformatics and gene trees. -Sequences of the SpmX genes in Table  S1  and members of the GH24 family were retrieved by BLAST searches on the Integrated Microbial Genomes and Microbiomes (IMG/M) database  [53]  and the National Center for Biotechnology Information (NCBI) "nr" database. See Tables  S1  and S5  for lists of gene ID numbers. Multiple alignments were achieved with MUSCLE  [54]  and manually adjusted and visualized with Jalview  [55]. Sequence conservation of SpmX residues was determined from the multiple sequence alignment of spmX alleles using ConSeq  [56]. To improve visualization of conservation patterns, the ConSeq scores were averaged across a 20-residue sliding window. For estimating bacterial species phylogeny, assembled genome data were obtained from the genome database of the National Center for Biotechnology Information  [57].