dataesr/scientific-paragraphs-categorization huggingface dataset:
Stress-related features were differentiated from histological ones based on their different orientation, consistency and regularity of crack borders, as well as through comparison with the pre-experiment scans and results reported in previous experiments (e. g. Bradfield 2013; Bradfield et al. 2016). Stress-related microfractures in cortical bone with a plexiform arrangement propagate along the line of vascular canals, which represent the path of least resistance, and in extreme circumstances these fractures may transect lamellar structures  (Wang et al. 1998). Orientation, position and linear dimensions of stress-related features were measured using VG Studio Max 2. 3 software. The minimum distance from the proximal end of each point to a microcrack was quantified, as was the length and maximum width of each crack (Table  2 ).
Acknowledgements This work was supported by the  Oppenheimer Memorial Trust , France-South Africa Research cooperation programme,  European Research Council  Advanced Grant, TRACSYMBOLS number  249587  awarded under the  FP7 programme , and the  LaScArBx , a research programme supported by the  ANR  ( ANR-10-LABX-52 ). The  South African Department of Science and Technology and National Research Foundation  provided funding for the high-resolution CT scanner and virtual imaging facilities at the  University of the Witwatersrand. This.
FUNDING This research received funding through a grant awarded to the project " DéPsySurdi " (PI: RP) by the  Fondation Maladies Rares' Social and Human Sciences program. OP's post-doctoral fellowship was supported by the " Who Am I?" Laboratory of Excellence # ANR-11-LABX-0071  funded by the  French Government  through its " Investments for the Future" program  operated by the  ANR  under grant # ANR-11-IDEX-0005-01.
g. , positive or negative, linear or nonlinear). A popular and widely tested approach to supervised machine learning is a method called "random forest"  (Breiman, 2001). This method allows for measuring the relative contribution of each specific variable, considering all the variables used in a dataset to predict the outcome of a "signal" (i. e. , a dependent variable) through a bootstrapping-type method, yielding a highly predictive accuracy and great deal of precision over the specification of control variables. In unsupervised machine learning, the algorithm infers a hidden function or pattern from the data, without regard to such a "signal" (which we typically refer to as the dependent variable). Supervised machine learning on the other hand differs from unsupervised machine learning in that the data patterns are derived by a "supervisory signal" (an outcome variable).
As the name implies, the "forest" consists of many such "trees". The method relies on "out of bag estimates" (bagging), which involves repeated sampling to form training datasets from an original dataset  (Breiman, 1996; Bylander & Hanzlik, 1999). The rest of the datasets in each case (the test datasets) are used to evaluate the predictive power of the variable importance and trees trained on the training dataset. The forest is then aggregated with each tree getting a "vote", which constitutes a weight in the ensembled model that summarizes all information from the trees. The outcome of this repeated sampling is captured in a variable importance list that indicates which variables are very likely to predict the outcome variable (for more technical discussions see  Breiman, 2001; IJzerman, Pollet, et al.
Method Pilot Study Participants and Procedure. Our questionnaire was programmed into the online platform Qualtrics and we collected data from mTurk (N = 143) and Prolific Academic (N = 148). Participants (all from US and UK; M age = 49. 45 (SD age = 7. 01; 37. 9% male, 61. 3% female) were asked to complete the survey between 9-11am, not eat or drink anything warm or cold for 10 minutes preceding the survey, and not exercise an hour preceding the survey. In our pilot study (where we could not easily control for other variables), we excluded all participants who did not adhere to these guidelines (mTurk N = 3; PA N = 56). When we used mTurk and Prolific Academic, mTurk had parameters that could be set to control the quality of participants (so called "qualification requirements") which Prolific Academic did not have at the time that we ran our study.
We had many more participants at Prolific Academic than mTurk that did not follow our instructions, which can likely be explained by the qualification requirements we could set a priori for mTurk. The total remaining N for the pilot study was 232 (M age = 49. 5, SD = 7. 04; 37. 5% male, 62. 5% female). Core body temperature was measured with an oral thermometer at the beginning of the questionnaire (Measurement 1) and at the end of the questionnaire (Measurement 2) by participants. To authenticate the temperature reading, participants took a picture of the thermometer (with date, time, and Measurement -1 or 2 -included; for an example photo uploaded by our participants, see Figure  2 ). 2 Survey details. Our dataset included a number of scales relevant to thermoregulation. In order to assess the importance of the quality of social networks (in relation to climate), we measured known correlates of core body temperature (from here on CBT) or behavior in response to temperature fluctuations, like self-reported stress ("stress" in the forest plot;  Cohen & Wills, 1985) , nostalgia ("nostalgia";  Routledge et al.
Each of these decision trees then allow for specification of the strength of predictor of variables based on a "desired outcome value" (the "supervisory signal", which is basically the dependent variable). Conditional random forests immediately engage in error-correction during the process, which means that, as a result, no training vs. test dataset is needed as happens in typical random forests. The forest is then aggregated with each tree getting a "vote", which constitutes a weight in the ensembled model that summarizes all information from the trees. This then creates a permutation variable importance (i. e. , the list with relative importance of each variable in predicting the outcome variable). For creating the classification trees, we relied on the R packages: tree  (Ripley, 2016) , lattice  (Sarkar, 2017) , plyr  (Wickham, 2016) , stargazer  (Hlavac, 2015) , and summarytools  (Comtois, 2016).
Participants In our cross-national study, we tested the interrelationship between climate, CSI, and CBT on a fairly large scale, including 12 different countries on 3 different continents, and 1,507 participants. We report all our exclusions in our data handling section. We also report all of the variables we measured in our study, except for two questionnaires intended for the development and validation of scales separate from this study. 3 The first author recruited collection sites through personal contacts and through "the ManyLab" (https://osf. io/89vqh/ Procedure. We created one central survey, which was translated and back translated keeping in mind loyalty to the original meaning (cf. ,  Brislin, 1970). All surveys were programmed into the online survey platform Qualtrics. Participants were run online or in the lab across our different sites.
63. Survey details. We used the same questionnaires as our pilot study, but added a few questions that seemed relevant for understanding the nature and structure of the relationship between CSI and CBT, like whether people are in a romantic relationship or not ("romantic"), how monogamous they perceive themselves to be ("monogamous"), and questions that pertain to the size of their online social networks (strength to one's online social identity; "onlineid" and strength of attachment to one's smartphone; "attachphone";  Yildirim & Correia, 2015). We also recorded participants' longitude and latitude via a standard option available in Qualtrics ("longitude"; we calculated latitude into equator distance "DEQ"). Finally, as the number of social contexts in which people are socially engaged may differ widely between cultures and language coding for "warm" and "cold" (Koptjevskaja-Tamm, 2015), we also included proxies for cultural influences with dummies for "language family" (Indo-European, Sino-Tibetan, and Uralic).
Because cultural influences may be similarly large within a language family when the same language is spoken in highly different longitudinal locations (such as English spoken in the US versus that spoken in Singapore), we also included degrees longitude. At the end of the survey, participants were thanked for their participation and debriefed. We again looked up minimum temperature of that day and average humidity of that day through their IP address and the weather history site. Data Handling. Before analyzing the data, for each scale variable, we checked the questionnaire's reliability, and corrected labeling differences between sites where necessary (a complete file with all alterations can be requested from the first author). We then created a final "raw" dataset. Next, we reviewed all pictures that participants uploaded to our Qualtrics platform.
We made 193 (mostly small) corrections to the CBT values, based on the picture participants uploaded. We also deleted 13 participants, as these participants uploaded either generic pictures or pictures that were irrelevant for our study. When no picture was uploaded, we kept the participant in our dataset. We also deleted participants from our dataset who reported temperature values ("CBT" variable in the dataset) lower than 34. 99 degrees Celsius, and participants that reported very unlikely temperature values (e. g. , 100 degrees Celsius). Our final sample consisted of 1523 participants. Because we had a far larger N than our pilot study, we were somewhat more liberal with our inclusion on the basis of time of day, and left participants in even when they were not within our requested time frame.
Our results thus show that distance from the equator (DEQ) and complex social integration (CSI) are amongst the most important predictors of core body temperature (CBT), close to being as important as sex and "language family", and more important than known benchmarks like height, weight, and stress. Furthermore, DEQ is an important predictor of CSI (Figure  5 ). Because DEQ and mintemp correlate highly, we chose to retain DEQ in our further analyses (but comparable results are obtained when using mintemp). Mediation by CSI. Based on these initial results and based on the relevance of distance from the equator and social integration, we decided to test a mediation hypothesis through a split-half cross-validation method. We created a training and test dataset through a random number generator in SPSS.
We explored our predictions in our training dataset, which we then confirmed in our testing dataset. For our mediation model, we were guided by our machine learning results. We hypothesized that distance from the equator would (positively) predict CSI, which in turn should (positively) predict CBT. CSI should then repress the (negative) relationship between DEQ and CBT. We conducted more traditional regression analyses to further understand the relationship between our most important variables. We did so again in the most robust way possible, by creating a training dataset (https://osf. io/6v9d7/) and a test dataset (https://osf. io/qs2pb/) and examining which hypotheses survived analyses in both datasets (splitting code can be found here: https://osf. io/q5tga/). To provide the highest informational value possible, we report here the analyses over the entire dataset (https://osf.
0053 degree Celsius. These results thus show a robust relationship between DEQ, CSI, and CBT: Having a more varied active set of social relations relates to a higher CBT, while distance from the equator decreases it. People further away from the equator also have a more varied set of social relations. Romantic relationships. Because our data are crosssectional and thus only allow indirect causal statements, we conducted a number of extra analyses to explore the interrelationship between DEQ, CSI, and CBT. These again followed the logic of relying on using first a training and then a test dataset. First of all, because having a romantic relationship was a key predictor of CSI, we explored the influence of romantic relationships and found in our conditional random forests that the effects differed for those who do and those who do not have a romantic relationship.
There are different possibilities for the mechanisms involved. Having a romantic relationship could provide the individual with an initial safe haven, making him or her less inhibited to explore and connect closely to others in various social contexts that can help protect core body temperature, as one would predict on the basis of attachment theory  (Bowlby, 1969). By contrast, having a romantic relationship could be a proxy for a reduced urgency to derive social warmth from CSI, in which case DEQ would be a weaker predictor of CSI for those with a romantic relationship. We saw the former conjecture (i. e. , explore and connect) supported in our conditional random forests and further confirmed in our mediation analyses. The mediation showing the relationship between DEQ, CSI, and CBT was moderated by having a romantic relationship or not in our training dataset (https://osf.
io/keqdu/) and in our testing dataset (https://osf. io/d2jhz/). In our full dataset, having a romantic relationship moderated the link between DEQ and CSI (B = -. 034, t = -6. 85, p <. 001, 95% CI [-0. 044, -0. 025]), as was predicted by our conditional random forests. These analyses showed that both for people with and without a romantic relationship, this relationship was significant in the full dataset. However, it was only significant in the training and test dataset for those with a romantic relationship, and thus, that is the only one we consider robust. For people with a romantic relationship, the link of DEQ with CBT is to a significant degree mediated by CSI (such that people who live further from the equator score higher on CSI; see Figure  6 ). For those without a romantic relationship, the effect appeared to be in the opposite direction (the further away from the equator, the lower the score on CSI), but this effect was, as stated, instable (see also Figure  7 ).
In hindsight, that the effect becomes smaller should not be surprising. The main reason for the lesser importance of height and weight is because there were correlations between height (r (full dataset) =. 146, p <. 001) and weight (r (full dataset) =. 144, p <. 001) with DEQ in our sample. This is well-known: Across endotherms, within the same taxa, body size correlates with distance from the equator (something that has become known as "Bergmann's rule"  (Bergmann, 1847) , which we thus also found in our data. Larger animals have a lower surface to body ratio, making them better able to stay warm in colder climates (something that is also true for modern humans;  Foster & Collard, 2013). This does not mean that height and weight are not important in protecting from the cold, on the contrary.
We present those dates through the sum of the probability distributions (SPDs)  69. SPDs are a standard method for representing chronological trends in radiocarbon datasets. They are produced by calibrating each independent date in the sample and adding the results to produce a single density distribution. This has the advantage of including the full range of probabilities associated with calibrated dates, instead of using single point estimates  69, 70. Here, we produced SPDs for the whole Llanos de Moxos and for particular archaeological cultures in the vicinity of the fisheries using OxCal v4. 2. 4 and the ShCal13 calibration curve  68, 71. A binning procedure was applied to account for sites that had multiple dates within a phase  69, 70, 72. Dates within sites were ordered, grouped within 100-year bins, and those occurring within the same bin were merged using the R_Combine function in OxCal.
in the Baures region and Franziska Riedel for facilitating the organisation of field work. This research was funded by grants to D. M. from the Institut Universitaire de France, the Institut Ecologie et Environnement (INEE)/CNRS (Projets Exploratoires Pluridisciplinaires program), the Mission pour l'Interdisciplinarité of the CNRS, the TOSCA committee (Terre Solide, Océan, Surfaces Continentales, Atmosphère) of the CNES (French National Center for Space Research), the Groupement de Recherche Mosaïque (GDR 3353, INEE/CNRS); and by grants to R. B. from the Institut Ecologie et Environnement (INEE)/CNRS (Projets Exploratoires Pluridisciplinaires TOHMIS), to L. R. from the Swiss National Science Foundation (SNSF) [grant no. P2BEP2_172250] and to U. L. from the European Union's Horizon 2020 research and innovation programme [Marie Skłodowska-Curie actions, EU project 703045].
This work was supported by public funds received in the framework of GEOSUD, a project (ANR-10-EQPX-20) of the program Investissements d'Avenir managed by the French National Agency. Research was also funded in part by the European Research Council project 'Pre-Columbian Amazon-Scale Transformations' (ERC-CoG 616179) to J. I. Data for Supplementary Figs S2 and.
Acknowledgements. We would like to thank  CERN  for supporting CLOUD with important technical and financial resources, and for providing a particle beam from the  CERN Proton Synchrotron. We also thank  P. Carrie ,  L. -P. De Menezes ,  J. Dumollard ,  K. Ivanova ,  F. Josa ,  I. Krasin ,  R. Kristic ,  A. Laassiri ,  O. S. Maksumov ,  B. Marichy ,  H. Martinati ,  S. V. Mizin ,  R. Sitals ,  A. Wasem  and  M. Wilhelmsson  for their important contributions to the experiment. This research has received funding from the  EC Seventh Framework Programme  ( Marie Curie Initial Training Network CLOUD-ITN  no. 215072 , MC-ITN CLOUD-TRAIN no. 316662 , the  ERC-Starting  grant MOCAPAF no. 57360 , the  ERC-Consolidator  grant  NANODYNAMITE no. 616075  and  ERC-Advanced  grant  ATMNUCLE no. 227463 ),  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no.
656994 , the  PEGASOS  project funded by the  European Commission  under the  Framework Programme 7  ( FP7-ENV-2010-265148 ), the  German Federal Ministry of Education and Research  (project nos. 01LK0902A  and  01LK1222A ), the  Swiss National Science Foundation  (project nos. 200020_152907 ,  206021_144947  and  20FI20_159851 ), the  Academy of Finland (Center of Excellence  project no. 1118615 ), the  Academy of Finland  ( 135054 ,  133872 ,  251427 ,  139656 ,  139995 ,  137749 ,  141217 ,  141451 ,  299574 ), the  Finnish Funding Agency for Technology and Innovation , the  Väisälä Foundation , the  Nessling Foundation , the  University of Innsbruck  research grant for young scientists (Cluster Calibration Unit), the  Portuguese Foundation for Science and Technology  (project no. CERN/FP/116387/2010 ), the  Swedish Research Council, Vetenskapsrådet  (grant  2011-5120 ), the  Presidium of the Russian Academy of Sciences and Russian Foundation for Basic Research  (grants  08-02-91006-CERN  and  12-02-91522-CERN ), the  US National Science Foundation  (grants  AGS1447056 , and  AGS1439551 ), and the  Davidow Foundation.
Flexural properties were determined using rectangular bars having dimensions of 127 mm × 12. 5 mm × 4 mm on the same machine, at a speed of 10 mm/min as per ASTM D 790. The fracture surfaces of the samples were studied using a Nova Nano SEM 450 Scanning Electron Microscope. Dynamic mechanical analyses were conducted using a RSA-G2 (TA Instruments, USA) in 3-point bending mode in the linear viscoelastic region (LVR). Rectangular samples (40 mm × 8 mm × 1. 2 mm) were prepared and investigated from 30-250 °C (3 °C/min heating rate), with a strain deformation of 0. 007% and a frequency of 1 Hz. The thermal stability of the samples was analyzed using a TGA Pyris 4000 from 30-800 °C (heating rate of 10 °C/min). Dielectric measurements were performed using a Novocontrol GmbH Concept 40 broadband dielectric spectrometer (Montabaur, Germany), and data were collected at room temperature over the frequency range of 0.
HN rel a b where Δε′ = ε′s -ε′∞ is the dielectric relaxation strength; ε′s and ε′∞ are relative permittivities at zero and infinite frequencies, f, respectively; ω, is angular frequency (=2 π f); trel is the relaxation time; and a and b are shape parameters describing the asymmetry of the dielectric function. Electrochemical impedance spectroscopy (EIS) measurements were performed in a NaCl solution (3. 5 wt. %) in a frequency range of 0. 01 Hz to 100 kHz with a wave amplitude of 5 mV at 25 °C. EIS data analysis was performed using Gamry Echem analyst software. Before conducting the EIS experiments, samples were immersed in the 3. 5 wt. % NaCl electrolyte for 30 min. The water contact angles (WCA) of the fabricated coatings were measured using Dataphysics (OCA 35, Germany) with 5 μL distilled water.
Dielectric Properties of Composites. To investigate the interfacial processes in the epoxy-based composites, the room temperature dielectric spectra over a broad frequency range were evaluated. The experimental data were then fit with Havriliak-Negami model, and parameter identification provided us with information on how the hybrid filler influenced the epoxy composites. Generally, in the composite systems, the Wagner-Maxwell-Sillars (MWS) relaxation occurs at low frequencies. The interfacial processes are intensified because of the hybrid filler addition. As seen in Fig. 6 , the peak maxima increase as the amount of filler increases up to 3 wt%. In addition, the relaxation time (Table  2 ) as a measure of the process activity decreases, indicating a strong interaction between the hybrid filler and matrix.
Acknowledgements The authors are grateful to the  EU  for providing a  Marie Skłodowska-Curie Individual Fellowship  ( H2020-MSCA-IF-2014-660489 ), to  BBSRC  ( BB/P026788/1 ) and to  EPSRC  ( EP/R021554/1 ). OF is a  Royal Society University  Research Fellow. PS acknowledges financial support from the  Agence Nationale de la Recherche  through the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'avenir program  ( ANR-10-120 IDEX-0002-02 ), by the  International Center for Frontier Research in Chemistry (icFRC).
EPJ Web of   , =        e J M m   (1)   ,        m J M m   (2) div 0  e J (3)   2 J div , 2 sf l l        m J m M m (4) Where  -electric potential, σ -conductivity, β - Here we take into account only bulk spin-dependent scattering, which would not change qualitative description of the phenomena presented in paper. The numerical simulation was provided with finite element method on the basis of Comsol Multiphysics ® software. For all simulations, we used the following values of the key parameters: σ F =0. 001 (Ω • nm) -1 ; σ P =0. 01 (Ω • nm) -1 ; ν=0. 1 (eV) -1 ; lsf P =100 nm; β=0. 7; Je=10 8 A/cm 2 ; lJ=1 nm, where indexes F and P refer to ferromagnetic and paramagnetic layers accordingly.
Methods Synthetic methods. 1Dy, 2Dy, 1Y, and 2Y were synthesized according to the published procedure  33. The solid solution 1Y 0. 95 Dy 0. 05 was synthesized following the protocol for 1Y, employing YCl 3 •6H 2 O and DyCl 3 •6H 2 O in a 19:1 molar ratio. The purity of the solid solution was verified using powder X-ray diffraction (Supplementary Fig. 27 ). The diffraction data were collected at room temperature on a Bruker D8 ADVANCE powder diffractometer operating in a 2θ-θ configuration using Co Kα radiation (λ = 1. 7902 Å). CF modeling. The CF modeling based on spectroscopic data, molar magnetization, and molar magnetic susceptibility was performed using the MagProp software  56. In the fitting procedure, the spectroscopic observations were included with their relevant uncertainties, while the molar magnetization and molar magnetic susceptibility data were included in M(BT -1 ) format.