dataesr/scientific-paragraphs-categorization huggingface dataset:
tinctorius 25%) and field seasons. Fast recovery of skin injuries was observed in the frogs that were recaptured after removing the tag. We tagged and tracked 311 frogs, located each frog multiple times a day (see further below), and recorded its position and any observed behavior. To record the behavior, we tried to visually spot the frog with every recorded position, although this was not always possible due to dense habitat. In these cases, we narrowed the location to approximately 1 m. All study plots were mapped using precision compasses and laser distance meters, establishing a network of labeled reference points (for method details, see  Ringler et al. , 2016a). To map frog movements, we measured distance and direction from these reference points and recorded data on digital maps with the GIS software ArcPad 10 (ESRI, Redlands, USA) on handheld devices (Vanquisher SV-86 rugged Windows tablet, Sinicvision Technology Co, Shenzhen, China; WinTab 9, Odys, Willich, Germany; MobileMapper10; SpectraPrecision, Westminster, CO, USA).
Occasionally, when frogs moved out of the mapped area, we recorded their location by averaging at least 30 GPS points with a GPS device MobileMapper 10. All data were collected as GIS spatial points with associated behavioral information, checked point-by-point at least twice, and corrected for errors such as wrong frog identities, duplicates, and impossible locations by one of the experimenters in GIS software QGIS  (versions 2. 14 and 3. 18, QGIS. org, 2022)  and ArcMap (various versions, ESRI, Redlands, USA). All suspect points where frog identity or location could not be unambiguously confirmed and corrected were removed.
Quantification of space use To quantify space use, we tagged 36 A. femoralis, 31 D. tinctorius, and 83 O. sylvatica, which we localized 6682 times and tracked for periods ranging from <1 to 45 days per individual. A subset of these data was used in previous publications on female space use in A. femoralis  (Fischer et al. , 2020)  and tadpole transport in D. tinctorius  (Pašukonis et al. , 2019). When a frog lost its tag, we attempted to recapture and retag the individual to continue tracking. Identity was confirmed based on unique dorsal or ventral coloration patterns. We excluded all frogs that were tracked for less than two full days (not including the tagging and tag removal/loss day) without any manipulation and handling (tag checks or retagging). We also removed short and temporally disconnected tracking periods when the frog was retagged.
In the end, we had data to quantify the space use of 29 A. femoralis (17 females and 12 males) tracked from 5 to 16 days (median = 14 days) and 26 D. tinctorius (11 females and 15 males) tracked for 3-45 days (median = 14 days). We tracked 29 O. sylvatica (14 females and 15 males) for 7-20 days (median = 10 days) within enclosures and an additional 37 O. sylvatica (20 females and 17 males) for 2-8 days (median = 3 days) at the natural site. The sampling rate (number of locations per day) varied for different datasets and tracking periods. Movements associated with specific behaviors of interest, such as the tadpole transport, were often sampled at a higher frequency. As the sampling rate influences the spatial parameters, we standardized the data by downsampling all datasets to match the datasets with the lowest sampling rate (~4 points per day).
An experienced observer (AP) down sampled the data in a two-step procedure that allowed us to maintain the maximum spatial and behavioral information. We first counted the number of points per day and selected the days with more than 2 points above the daily average for the respective dataset. We then removed redundant points while trying to keep the most spatially and temporally distributed points, and points with rare behaviors, such as parental care and mating. In a second step, we automatically downsampled the remaining dataset to a minimum sampling interval of 60 min while retaining intermediate points for long (>20 m), fast movements, occurring within shorter than 60 min. The resulting dataset had 3-7 points per day per frog (median = 4). To quantify the space use, we calculated the daily cumulative distance traveled (daily travel hereafter) for 84 frogs tracked for at least two full days.
For 76 frogs tracked for a minimum of 7 days, we also quantified the maximum movement extent area (movement extent hereafter) as a minimum convex polygon (MCP) and the home range as 95% utilization density (UD) contour derived from kernel density estimation (KDE) using the 'mcp' and 'kernelUD' functions of the 'adehabitatHR' R package  (Calenge, 2006). The smoothing parameter for the KDE was calculated with a conservative plug-in bandwidth selection method with the 'hpi' function of the 'ks' R package  (Chacón and Duong, 2018). Daily travel is a good proxy for the mobility of the animal, but it does not capture the range of the animal's movement. Movement extent calculated as the MCP encompassing all tracked positions is a proxy for the size of the area explored by an animal including the rare excursions, but it tends to overestimate the size of the area familiar to the animal.
Our tracking-based space use measures represent a snapshot of an animal's long-term movement, and some species were tracked in experimental study plots confined by enclosure or water. Therefore, we further validated our tracking data described above with three supplementary datasets, including multiyear capture-recapture data in natural populations of A. femoralis and D. tinctorius (for more details on the capture-recapture method see  Ringler et al. , 2009)  and a short-term tracking of O. sylvatica in a natural population. For A. femoralis and D. tinctorius, we compared the sex differences observed in the tracking data to a multiyear capture-recapture data collected at the same study site over 3  (2009 -2011 for D. tinctorius, B. Rojas, M. Ringler, unpublished data)  or 6 years (2014-2019 for A.
femoralis, M. Ringler, E. Ringler, A. Pašukonis, unpublished data). For this analysis, we only included the individuals that were recaptured at least 30 days apart. For each of these recaptured individuals, we calculated the maximum linear distance between all recapture points. Maximum linear distance is correlated to the movement extent area calculated for tracked individuals, but it can be calculated from as few as two recaptures of an individual. We also calculated the maximum linear distance for each individual based on the tracking data. Contrary to the tracking data for A. femoralis, which was collected on an island with artificial pools, A. femoralis capture-recapture data were collected from a nearby natural population and thus served as additional validation for the effects of the island and artificial pools on the patterns of space use.
(2)  To estimate the explored area, we calculated the total area within 5 m around the movement trajectory. The value is based on a putative perceptual range of a frog being at least 5 m. (3) Trajectory straightness was calculated as the ratio between the straightline distance from the release site to the end of the homing trajectory and the cumulative distance of the actual homing trajectory. To calculate explored area and trajectory straightness, trajectories were downsampled to the minimum sampling interval of 15 min. Trajectory straightness measures are influenced by the sampling rate, which was variable in our dataset. Therefore, we also quantified homing accuracy as an angular deviation from the home direction at fixed distance thresholds from the release site. The goal was to quantify the accuracy during the initial orientation near the release site, adjusted for translocation distance.
Weather variables We measured the understory ambient temperature with temperature-data loggers (HOBO U23 Pro v2, Onset Computer Corp, Bourne, USA) placed ~30 cm above ground and recording temperature at 15 or 30 min intervals. Because our study species are exclusively diurnal, we calculated daytime temperature by averaging all measures from sunrise to sunset. At the O. sylvatica enclosure site, we manually measured temperature three times per day at the start, middle, and end of each tracking session with a handheld electronic thermometer (GFTH 95, GHM Messtechnik, Regenstauf, Germany) held slightly above the ground. We averaged the three measurements to obtain daytime temperature. We also calculated mean daytime temperatures for each frog during the navigation experiments by averaging daytime temperatures over the tracking period of each frog (also see Supplementary file 1, Table  1l ).
Data analyses All statistics were generated in R Studio (version 1. 0. 153, RStudio Team, 2020) running R  (version 3. 6. 3, R Development Core Team, 2020). Space-use plots were generated in QGIS (version 2. 14, QGIS. org, 2022), box plots, trajectory plots, and bar plots were generated in R with the 'ggplot2' package  (Wickham, 2016) , circular plots with the 'circular' package  (Agostinelli and Lund, 2022) , and model plots with the 'sjPlot' package  (Lüdecke, 2021).
Space use Spatial variables (daily travel, movement extent area, movement extent distance, home range, and explored area) approximately followed a log-normal distribution and thus we transformed the raw data using the natural logarithm to fit the model assumptions. Before all other analyses, we evaluated if tagging-related skin injuries affected frog mobility. We categorized the injuries into three categories of severity: no injury, superficial abrasion, and skin lesion. We then included injury as a categorical predictor in models of home range size, movement extent, and daily travel. Injuries had a significant effect on home range size and daily travel in A. femoralis, but not in D. tinctorius and O. sylvatica. Based on these results, we excluded A. femoralis (one male and one female) with skin lesions from space use analyses.
sel' function within the 'MuMIn' R package  (Bartón and Barton, 2020). Because parental behaviors mostly occurred in one sex, we also analyzed the influence of behavior on movement for each sex separately. We fitted separate LMMs for each sex of each species with behavior as a fixed factor (two or three levels: parental, mating, and other as appropriate for each species and sex), and frog identity and tracking date as random factors. The daytime temperature was only included as a fixed factor if the best model based on AICc included temperature (it did for A. femoralis, but not for D. tinctorius and O. sylvatica). We used least-squares means contrasts to compare daily travel between behavioral categories. Post hoc comparisons were done with the 'emmeans' function within the emmeans R package  (Lenth et al.
To investigate sex differences in the homing success we fitted generalized linear models (GLMs) with binomial error distribution and Logit link function using the 'glm' function within R package stats  (R Development Core Team, 2020). We used the homing success as a binary response variable with sex, frog weight (except for O. sylvatica), and the mean daytime temperature during the entire tracking period as predictors. To investigate sex differences in exploration, we fitted an LM with the natural log-transformed explored area as the response variable and sex, mean daytime temperature, and frog weight as predictors. To investigate sex differences in homing trajectory straightness we fitted a beta regression model for proportions via maximum likelihood using the 'betareg' function within the R package betareg  (Cribari-Neto and Zeileis, 2010).
Acknowledgements We are grateful to the staff of the  CNRS Nouragues Ecological Research Station, Nouragues Nature Reserve, Fundación de Conservación Jocotoco ,  Reserva Canandé ,  Centro Jambatu de Investigación y Conservación de Anfibios, and Wikiri  for logistic support and generously providing access to the study sites. We are deeply grateful to these organizations and their dedicated staff for their commitment to preserving our natural world and facilitating research. We thank  Kristina Beck ,  Steffen Weinlein ,  Susanne Stückler ,  Eva K Fischer ,  Italo Tapia , and  Vincent Premel  for assistance in the field,  Jinook Oh  for coding the ' Frogger Logger' Python script ,  George Perry ,  Edward Clint , and one anonymous reviewer for constructive feedback on the manuscript, and  Walter Hödl  for continuing inspiration and mentorship that has led to this study.
This work is part of a partnership agreement between AP, BR, MR, ER, and the  Nouragues Nature Reserve  to improve and spread the knowledge about amphibians. The study was funded by The  European Commission 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Actions  grant agreement no. 835530  to AP, LAO, and  Simon Benhamou ;  National Science Foundation   CAREER grant  ( IOS-1845651 ) to LAO; Association for the  Study of Animal Behaviour (ASAB) 2016 Research Grant  to AP;  Austrian Science Fund (FWF) :  P24788  and  P31518  and  FWF Herta-Firnberg  Grant  T699  to ER;  P33728  and  FWF Erwin-Schrödinger Fellowship   J3868-B29  to MR;  FWF Erwin-Schrödinger Fellowship   J3827-B29  to AP;  CNRS Nouragues Travel Grants (AnaEE France   ANR-11-INBS-0001 )  NTG2009  and  NTG2010  to BR;  NTG2015  to AP and BR.
LAO is a  New York Stem Cell Foundation -Robertson Investigator. ML received funding by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 79809. BR received funding from the  Academy of Finland   Research Fellowship  (no. 319949 ). CR was funded by  FWF-DK  project  W-1262  (Speaker:  Tecumseh Fitch ). LAC acknowledges the support of  Wikiri  and the  Saint Luis Zoo. The  Nouragues Ecological Research Station , managed by  CNRS , benefits from ' Investissement d'Avenir ' grants managed by the  Agence Nationale de la Recherche  ( AnaEE France   ANR-11-INBS-0001 ;  Labex CEBA   ANR-10-LABX-25-01 ). Andrius Pašukonis National Science Foundation IOS-1845651  Lauren A O'Connell Association  for the  Study of Animal Behaviour 2016 Research Grant Andrius Pašukonis.
Identification of the clay minerals was based on their main diffraction peaks and by comparing the three diffractograms obtained. The proportion of each clay mineral on glycolated diffractograms was measured using the MACDIFF 4. 2. 5. software  (Petschick, 2000). Identification of the clay minerals follows the methods in  Deconinck et al. (2019)  and  Moore & Reynolds (1997).
These data were then scaled up to the known quantity of the sample according the method of  Belcher et al. (2005). freshwater algae, marine palynomorphs, structured phytoclasts, unstructured phytoclasts, black debris, amorphous organic matter (AOM), and charcoal (further described in  Hollaar et al. , 2021). The palynofacies were quantified on a palynological slide using the optical light microscope (40x10 magnification) and counting a minimum of 300 particles per slide. Because the samples are AOM-dominated, counting was continued until a minimum of 100 non-AOM particles were observed. We used the percentage of terrestrial phytoclasts, which includes sporomorphs, structured and unstructured phytoclasts, to examine changes in terrestrial organic particle content.
Statistical analysis Principal component analysis (PCA) was performed in the software PAST on the normalized dataset including microcharcoal, TOC, CaCO3, δ 13 Corg, S/I, K/I, primary clay mineralogy, Si/Al, Zr/Rb. The samples before the +ve CIE (951. 0-930. 4 mbs) and the samples after the +ve CIE (930. 3-918. 0 mbs) are grouped to examine a potential difference in the sedimentary composition before and after the +ve CIE. A Pearson's correlation was executed in Matlab R2017b. The p value tests the hypothesis of no correlation against the alternative hypothesis of a positive or negative correlation (significance level at p ≤ 0. 05).
PCA analysis The proxy datasets (δ 13 Corg, TOC, percentage terrestrial phytoclasts, microcharcoal, smectite/illite, kaolinite/illite, abundance of chlorite and R1 I-S, Si/Al, Zr/Rb, Zr/Al) were normalized between 0-1 and run for Plotting PC-1 (y-axis) over PC-3 (x-axis) shows that the samples after the onset of the +ve CIE are grouped to the top of the y-axis (more associated with S/I compared to K/I) and to the right of the x-axis (more associated with primary minerals, phytoclasts, and higher Si/Al, Zr/Rb and Zr/Al) (Fig. 5 ).
Acknowledgements: This is a contribution to the  JET  project funded by the  Natural Environment Research Council (NERC)  (grant number  NE/N018508/1 ). SPH, CMB, JFD, CU, ML and TPH, acknowledge funding from the  International Continental Scientific Drilling Program (ICDP)  and TPH acknowledges funding from the  University of Exeter. We thank the  British Geological Survey (BGS) , especially  James Riding ,  Scott Renshaw  for facilitating access to the Mochras core. Also,  Simon Wylde  and  Charles Gowing  for their contribution to the XRF scanning and discussion on the results. We further thank  Chris Mitchell  for help with the TOC and δ 13 Corg analyses. Finally, we thank  Ludovic Bruneau  for technical assistance with the XRD-analysis. https://doi. org/10. 5194/cp-2022-87Preprint. Discussion started: 30 November 2022 c Author(s)  2022.
Data availability: Supplementary data is available at the National Geoscience Data Centre at Keyworth (NGDC) at (doi to be added) for the interval 934 -918 mbs. All data presented for the interval 951 -934 mbs is available at the National Geoscience Data Centre at Keyworth (NGDC) at https://doi. org/10. 5285/d6b7c567-49f0-44c7-a94c-e82fa17ff98e (Hollaar et al. , 2021). The full Mochras XRF dataset is in  Damaschke et al. (2021). Author contribution: CMB, SPH and TPH designed the research. TPH conducted the laboratory measurements, with JFD contributing to the XRD-measurements and MD, CU and ML to the XRFmeasurements. TPH, CMB and SPH wrote the manuscript, with contributions of all authors.
The situation is considerably more complex for crystalline materials, for which additional parameters such as the crystal direction or, in the case of polymorphism or polytypism, even the specific crystal structure, affect the elastic properties. For these materials, Young's modulus can, in principle, be calculated via the elastic constants of the crystal. 13 However, its determination may be impeded by the lack of literature values of the elastic constants for the crystal structure under investigation, such that the database for theoretical values is scarce. This is seen for the ternary semiconductor alloy In 1Àx Ga x P, where even the gallium content x influences Young's modulus. 13 For 3C-SiC, another crystalline material, theoretical predictions vary between 125  14  and 466 GPa,  15  even surpassing the spread of experimentally determined values, because the literature provides differing values of the elastic constants.
The authors acknowledge the financial support from the  European Unions Horizon 2020 program for Research and Innovation  under Grant Agreement No. 732894  ( FET Proactive HOT ) and the  German Federal Ministry of Education and Research  through Contract No. 13N14777  funded within the  European QuantERA cofund project QuaSeRT. We further acknowledge financial support from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) through Project -ID 425217212 -SFB 1432  and via Project No. WE 4721/1-1  as well as project  QT-6 SPOC of the Baden-W€ urttemberg Foundation.
2013). Demultiplexed sequences were trimmed to 150 base pairs, and then selected for ASV (Amplicon Sequence Variant) picking using the Deblur pipeline v. 1. 1. 0 (Amir et al. 2017 ). In the pipeline, de novo chimeras were identified and removed, artifacts (i. e. PhiX) were removed, and ASVs in less than 10 samples were removed for further analyses due to low representation. Blank samples were used as negative controls to determine the read count threshold for downstream analyses. ASV tables were rarefied to a sequencing depth of 1285 reads, leading to the removal of 15 of 1834 non-blank samples which contained fewer reads than the set depth. Analysis was completed using both QIIME 2 2021. 2 (Bolyen et al. 2019 ) and in R 3. 4. 2 via the phyloseq package  (McMurdie and Holmes 2013).
2015). Each time series plot was then decomposed into three components (trend, seasonality, and error) using STL (Seasonal-Trend decomposition procedure based on Loess;  Cleveland et al. 1990 ). Analyses involving the identification of seasonal dynamics within sample types were then detrended according to the STL outputs (to remove any confounding effects of year on year changes in community dynamics), and the resulting time series were statistically tested for seasonality using Friedman tests with Benjamini-Hochberg corrections. Analyses and visualizations were performed using the tsutils (https://github. com/trnnick/tsutils) and forecast  (Hyndman et al. 2022 ) packages in R.
Differential abundance analysis To identify specific microbial ASVs driving the observed patterns of seasonality in water and effluent samples while accounting for compositionality, we conducted differential abundance analyses on the non-rarefied dataset using Songbird v. 1. 0. 4 through QIIME2 v. 2021. 2  (Morton et al. 2019). The differential abundance models were trained with the water and effluent samples using only the data from August and March time points. Songbird differential rankings were visualized in Qurro  (Fedarko et al. 2020)  to identify the 10 most differentially abundant ASVs in March relative to August, and the 10 most differentially abundant ASVs in August relative to March, separately with both water and effluent samples. Once March-associated and Augustassociated ASVs were identified, we examined changes in their log-ratios across the entire rarefied dataset of water and effluent samples to test whether the identified ASVs follow a predictable seasonal gradient through time.
Statistical analysis of physicochemical data We examined the effects of the wastewater disinfection treatments on the abiotic water characteristics of sites immediately downstream of the treatment plants. This was accomplished by testing for significant differences in key physicochemical variables (temperature, pH, Dissolved oxygen, NO2 -/NO3 -, NH3, PO4 3-, volatile suspended solids, conductivity, and turbidity) between pre-intervention time points  (2013) (2014) (2015)  and post-intervention time points  (2016) (2017) (2018) (2019). This analysis was conducted using site 76 (immediately downstream of Calumet), site 56 (immediately upstream of Calumet), site 36 (immediately downstream of O'Brien), and site 112 (immediately upstream of O'Brien) (Supplementary figure  S3 ). Like the fecal coliform data, most of the measured physicochemical variables contained skewed sampling distributions and substantial outliers.
This aspect is especially relevant for new diseases such as COVID-19, where physician experience is lacking. Therefore, a supportive prognostication model that can integrate baseline data with complex, dynamic processes in an objective manner is necessary. Machine learning (ML) algorithms could be used to address this need, as some have successfully been evaluated in clinical settings such as in cardiovascular intensive care  [14]. Wernly et al  [9]  retrospectively analyzed arterial blood gas data from septic intensive care patients from a multicenter electronic ICU database as well as from a single-center MIMIC-III (Medical Information Mart for Intensive Care) data set to predict 96-hour mortality.