dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, d is simply cc discriminated by species, which means the name of the species present within the forest plots was recorded, as well. Following the field protocol, two field operators estimated the cc and d visually. When their estimation was similar, the average value was recorded; otherwise, they tried to reach an agreement. It is worth mentioning that the measurements at the single tree and plot level were carried out within the inner and outer circle, respectively (Figure  1c ). Information collected by a forest inventory usually has lower location accuracy than that provided by lidar systems. To improve it, a local geodetic network made of 41 pairs of GPS-derived points was built on the same map projection as the lidar data  [20]. All plot centers, as well as the tree positions within the outer circle were surveyed using a total station theodolite.
Lidar Inventory In this section, we describe the lidar dataset acquired over the study site (Section 2. 3. 1) and the methods used to extract forest metrics from the 3D lidar point cloud, i. e. , the lidar forest metrics inventory (Section 2. 3. 2). These methods were published in Ferraz et al. [14]  and Ferraz et al. [15] , and their reliability to retrieve relevant forest metrics was already assessed. As a result, they are only briefly described here. Thus, instead of focusing on the methods themselves, our effort is directed at analyzing their reliability for the extraction of the forest metrics that are further used in AGB estimates (e. g. , tree density, tree height, forest layers mean height and crown cover).
Lidar Data Measurements The lidar data were acquired on 14 July 2008, in a full-waveform mode using a LiteMapper 5600. The footprint and the scan angle have the values 0. 3 m and ±22. 5 • , respectively. A strip overlap of 70% guaranteed "wall-to-wall" coverage. With a single run pulse density of 3. 3 points per square meter (pts/m 2 ), the final average pulse density within each forest plot is 9. 5 pts/m 2 (min = 4. 7, max = 15. 5, σ = 1. 9 pts/m 2 ). The photons corresponding to each pulse were reflected by the different features of the landscape and recorded into a waveform structure. The digitized waves were processed using the RiANALIZE software (http://www. riegl. com/products/software-packages/rianalyze/) in a workstation; each pulse gave rise to 1-5 lidar returns. The position and orientation of the platform, which are given by onboard GPS/IMU hybrid measurements, were corrected by analyzing overlapping laser strips from the calibration cross-strips.
In this study, we used a layer-specific mean bulk density ( bd) that applies to the entire study area. Firstly, species that populate each layer were identified using the field inventory dataset (Section 2. 3. 2). The corresponding reference bulk densities given in Simões  [24]  were assigned to each species. The average of the bulk densities that populate each layer was computed in order to estimate the corresponding bd. The values were 1. 76, 2. 21 and 2. 46 kg•m -3 for juvenile overstory, understory and ground vegetation, respectively. Therefore, to estimate the AGB for single layers using the lidar approach, we set bd = bd in Equation (  6 ).
Acknowledgments: This work was supported in part by the  Portuguese Foundation for Science and Technology  under Grant  PTDC/AGR-CFL/72380/2006 , co-financed by the  European Fund of Regional Development (FEDER)  through  COMPETE-Operational Factors of Competitiveness Program (POFC)  and the Grant  Pest-OE/EEI/UI308/2014. The work of  António Ferraz  was supported in part by the  Jet Propulsion Laboratory  through the  NASA   Postdoctoral Program , which was administrated by the  Oak Ridge Associated Universities  through a contract with  NASA.
; 21. 3% Ϯ 2. 34 with Mycobacterium rhodesiae NBB3 (22) and Mycobacterium tusciae (23); 21. 1% Ϯ 2. 33 with Mycobacterium austroafricanum (24) and Mycobacterium vanbaalenii (25); 20. 8% Ϯ 2. 33 with Mycobacterium gilvum Spyr1 (26); 20. 5% Ϯ 2. 32 with Mycobacterium aurum (27); 20. 3% Ϯ 2. 31 with Myco- bacterium fortuitum ATCC 6841 (28); 20% Ϯ 2. 31 with Mycobac- terium neoaurum DSM 44074 (29); and 19. 9% Ϯ 2. 3 with Myco- bacterium marinum E11 (30). Accession number(s). The M. acapulcensis genome sequence has been deposited at EMBL under the accession numbers LT592221 to LT592249.
Grade 1: severe pain, swelling not exceeding the elbow or knee. Grade 2: edema exceeding the elbow or knee. Grade 3: edema reaching or exceeding the root of the limb. Snakebites were considered asymptomatic in cases without pain and fang mark. Decrease in the number of blood platelets (thrombocytopenia) was investigated in case of viper syndrome. Data on coagulation tests (WBCT, fibrinogen) are not requested routinely and very rarely investigated. We also analyzed the cases that received immunotherapy: FAV-Afrique® (produced by Sanofi Pasteur, France), the only antivenom available in Morocco since August 2012. It is a polyvalent antivenom composed of highly purified fragments of F(ab') 2 immunoglobulins against ten snake species among the most dangerous in Africa and belonging to families Viperidae and Elapidae.
The International Atomic Energy Agency (IAEA) has supported the development of an Excel™ based software which allows the conversion of FRN inventories into soil redistribution rates, through the application of different models (among them the PDM, the DMM and the MBM). The software was initially developed for 137 Cs and later extended for the application of 7 Be and 210 Pb ex  (Walling et al. , 2014). The software is public and freely downloadable, but its code is not editable and thus cannot be adapted to specific environmental conditions (e. g. a particular depth profile of the FRN distribution) or be used for other FRN such as 239þ240 Pu. The aim of this study is to present a new conversion model, called MODERN (Modelling Deposition and Erosion rates with Ra-dioNuclides). The MODERN model is based on a unique algorithm to convert FRN inventories into both erosion and deposition rates.
, 2012 ; for all data used see Table  2 ). The detailed description of the investigated site, the specific sampling design and the results obtained in the frame of this study have been reported by  Ceaglio et al. (2012). Soil samples were collected along a main avalanche path in 2010. In the track area, two transects (TA1 and TA2) were sampled, with 5 cores collected within a distance of 15 m to each other. As for the reference sites, 3 reference samples, located in a nearby flat area, were collected, cut in 3 cm depth increments, and used to determine average profile distribution and maximum depth of 137 Cs. All samples were oven-dried at 40 C, lightly ground and sieved and 137 Cs activity was measured with a Lithium-drifted Germanium Detector. A least squares exponential fit of the 137 Cs depth profile at the reference sites is characterized by R 2 of 0.
Acknowledgements This work was funded by the  Swiss National Science Foundation (SNF) , project no. 200021-146018. The authors also acknowledge the  International Atomic Energy Agency (IAEA)  for support provided through the technical project 20435 and the Coordinated Research Project (CRP) D1. 50. 17 on "Nuclear techniques for a better understanding of the impact of climate change on soil erosion in upland agro-ecosystems". Finally, the authors express their sincere gratitude to  Dr. Enrico Chiaradia  from the  Department of Agricultural and Environmental Sciences of the University of Milan (Italy) , for his contribution in developing the R code of the MODERN model.
Acknowledgements We are grateful to  Mrs Carol Kyle  ( James Hutton Institute ) for providing excellent technical assistance and to the staff of the  James Hutton Research Institute  for their assistance in the management of experimental animals. We are also grateful to  Dr Alan Law  for his statistical guidance and input. This work was funded by the  Wellcome Trust  ( 080388  to PAF, SMR, CC, NPE and RMS). Received 1 July 2016, revised 21 October 2016, accepted 1 November 2016.
Fitting this model on a dataset of 105 placental mammals, using both node-dating and tip-dating approaches, suggests that the two pure clocks, Brownian and white noise, are rejected in favour of a mixed model with approximately equal contributions for its uncorrelated and autocorrelated components. The tip-dating analysis is particularly sensitive to the choice of the relaxed clock model. In this context, the classical pure Brownian relaxed clock appears to be overly rigid, leading to biases in divergence time estimation. By contrast, the use of a mixed clock leads to more recent and more reasonable estimates for the crown ages of placental orders and superorders. Altogether, the mixed clock introduced here represents a first step towards empirically more adequate models of the patterns of rate variation across phylogenetic trees.
To account for such complex patterns of rate variation, in this article, a mixed clock model is introduced. According to this model, rate variation has both a long-term Brownian behaviour ( presumably driven by long-term changes in lifehistory strategies) and short-term variations, the latter being captured by a white noise process. By tuning the relative variance of its two components, the mixed clock interpolates between the uncorrelated and correlated relaxed clocks, containing them as particular cases. Applying this mixed clock model to an empirical dataset leads to a simple measure of how the total variation in substitution rate across the tree is partitioned into the Brownian and the white noise components, which then yields potentially useful insight about how rate variation unfolds over different time scales.
These observations justify the use of an independent assessment of alternative relaxed clock models in the context of a node-dating analysis with rich fossil calibration, before transposing the conclusions to the tip-dating case. (b) Empirical analyses using the node-dating approach A standard node-dating approach was first conducted, using a modified version of the dataset of Meredith et al. [34] , a large set of hard fossil constraints and under two alternative relaxed clock models, the autocorrelated Brownian (lognormal) clock  [5]  and the uncorrelated white noise clock  [2]. The estimates under the two relaxed clock models are globally similar (tables 1 and 2; electronic supplementary material, figures S1,S2). At the level of orders and superorders, the main differences concern the age of Eulipotyphla, Euarchontoglires, Afrotheria and Placentalia which are all older by approximately 10 Myr under the white noise clock than under the Brownian clock (tables  1  and 2  The tendency of the uncorrelated clock to lead to more uncertain estimates of divergence times compared with the autocorrelated clock can be understood in terms of the shrinkage power of the two alternative priors over rates.
(c) Mixed clock analyses using the node-dating approach When applied to this placental dataset and in the node-dating settings, the mixed clock captures both an autocorrelated and uncorrelated component. Specifically, the Brownian component is inferred to contribute 61% of the total variance across branches, thus indicating that long-term autocorrelation accounts for a major part of rate variation. On the other hand, this still leaves a significant fraction (39%) of the total variation accounted for by the white noise process, suggesting that short-term fluctuations or, in any case, deviations from the Brownian assumption, are substantial. These inferred proportions are much larger than those obtained in the context of simulations under purely Brownian or purely white noise clocks, where at most 3% of the total rate variation is attributed the incorrect component (see above, section Simulations).
(d) Tip-dating analyses The placental dataset was further analysed in a tip-dating context, using a modified version of the datasets of O'Leary et al. [35]  and Meredith et al. [34]. Analyses were conducted without discrete morphological data, so as to analyse the patterns of molecular rate variation and to better characterize the behaviour of the mixed clock model in a simple case where only molecular sequences are considered. One first major observation that can be made in this context is the strong sensitivity of divergence time estimation to the choice of the relaxed clock model (tables  1  and 2 ). On the one hand, the estimation under the white noise clock model (electronic supplementary material, figure  S7 ) tends to give estimates that are globally younger than those obtained using the node-dating approach.
Discussion (a) Mixing autocorrelated and uncorrelated clocks In this work, a mixed relaxed clock model was introduced, which can be mechanistically interpreted as a rate variation process undergoing short-term fluctuations on the top of Brownian long-term trends. Statistically, this mixed clock represents an alternative solution to the problem of choosing between autocorrelated and uncorrelated relaxed clocks, by proposing instead to combine their respective merits, thus simultaneously capturing the long-term autocorrelation signal while accommodating the local deviations. This mixed relaxed clock was applied to a dataset of 105 extant placental mammals, using two alternative dating paradigms: node-dating, with a rich set of fossil constraints and tip dating. In the case of the node-dating analysis, divergence times are relatively well constrained by fossil calibrations.
Methods (a) Data, taxon sampling, phylogeny and fossil calibrations The nucleotide dataset of Meredith et al. [34]  was first restricted to placental mammals and then reduced to 105 taxa in a way that was intended to better correspond to the diversified sampling assumption. Specifically, a cut-off time of 25 Myr was chosen. Then, based on the time-calibrated phylogeny published by Meredith et al. [34] , only one terminal taxon was chosen for each lineage crossing the cut-off time. For the tip-dating analyses, the 105 taxon dataset was combined with the set of eutherian fossils of the analysis of O'Leary et al. [35] , excluding Hapalops, which is younger than 25 Myr. This resulted in a total of 138 eutherian species. Altogether, this taxon sampling can be seen as approximating a uniform fossil sampling rate between the origin of Eutheria and 25 Myr, followed by a fossil sampling rate of 0 between 25 Myr and the present.
(f ) Implementation The models described above were implemented using the previously developed COEVOL program  [14, 20, 25]. This programming environment uses an explicit graphical model representation  [53] , which makes the development of new models, such as the mixed clock introduced here, relatively straightforward. The detailed MCMC algorithms used in this program are described by Lartillot & Poujol  [20]. Briefly, an MCMC cycle starts by sampling a detailed substitution history for all sites and over the whole tree from the conditional posterior distribution defined by the current parameter configuration. This stochastic mapping of substitution events  [58]  is technically a data augmentation, for which compact sufficient statistics (total number of substitution events between each possible pair of nucleotide states, total time spent in each state) can be calculated separately for each branch and summed over all sites.
(g) Simulations In the case of node-dating, simulations were conducted using the posterior predictive formalism. Specifically, the MCMC sampler was run on empirical data under each of the three models (pure Brownian, pure white noise or mixed clock). In a second step, for each run, 10 regularly spaced points from the MCMC (burn-in excluded) were used as parameter configurations for simulating 10 replicates of the original dataset. This resulted in a total of 30 simulation replicates (10 under each relaxed clock model). These simulated data were then re-analysed under the mixed clock model. For each replicate, the posterior mean percentage of variance contributed by the white noise component was estimated and compared to the true value. The true value is trivially 0% for the pure Brownian model and 100% for the pure white noise model.
A second series of simulations were conducted, which was identical to the first series, except that the extinction rate was constrained to be equal to 0 and the diversified sampling option was deactivated during the run under the prior, thus emulating a Yule prior without diversified sampling. A final series of simulations was conducted using the posterior predictive formalism, as in the case of the nodedating analysis. All these 90 replicates were re-analysed under the mixed clock model, using the serial birth-death prior. Thus, the first series of simulations is under matching prior over divergence times, the second under mis-specified prior over divergence times and the third is most faithful to the empirical data. To reduce the computational burden, the whole simulation experiment was conducted based on a reduced version of the original dataset, using only the BRCA2 partition (5046 aligned nucleotide positions) for the complete set of 105 taxa.
Authors' contributions. M. P. gathered the data and provided the palaeontological expertise behind the present analysis. F. R. helped in running independent analyses in REVBAYES and MRBAYES, both for preliminary investigation and for confirmation of the results presented here. N. L. designed the mixed clock model, implemented the Monte Carlo rstb. royalsocietypublishing. org Phil. Trans. R. Soc. B 371: 20150132 methods, applied the model to empirical data and performed the simulation study. N. L. wrote the first draft of the manuscript; the final manuscript was the joint effort of all authors.
Statistical analyses All statistical analyses were performed using SAS software version 9. 3 (SAS Inc. , Cary, NC). When required, data were log 10 -transformed before analyses to fulfil assumptions of normality and homoscedascity. Because metabolic rates are generally influenced by body mass (Hill et al 2012), we initially tested whether this was the case in our study using three regression analyses in which body mass was regressed against, respectively, resting and maximal metabolic rates, as well as metabolic scope. We restricted these analyses to those in control groups, calculated averages in metabolic parameters over the measures obtained at 24h, 48h and 168h (7 days) post-challenge. We then tested whether resting and maximal metabolic rates, and metabolic scopes of LPS-injected fish differed from those of controls using multivariate general linear mixed models with treatment (LPS or PBS), time, and their interaction, and with initial mass as fixed effects, and with individual as the random effect; for each time point, differences between LPS-challenged and control fish were contrasted within the same model using the "estimate" statement.
This research was supported by a  Marie Curie Reintegration Grant  ( FP7-PEOPLE-IRG-2008  # 239257 ) to  Dr Camille Bonneaud ;  Natural Environment Research Council  ( NE/M00256X ) to  Dr Camille Bonneaud ;  Australian Research Council Discovery Projects  ( DP120101215 ) to  Prof Frank Seebacher  and  Prof Robbie Wilson. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
E X P E R I M E N T A N D DATA S E T From mid-January to mid-February 2014, four sites (hereafter referred to as site#) were equipped with a 1 s short-period seismic sensor (Lennartz LE3Dlite) which benefits from a flat spectral amplitude response between 1 and 80 Hz. Each of the seismic sensors was collocated with a single-frequency GPS module (u-blox LEA-6T) embedded into a device named GEOCUBE and developed by the French National Institute of Geography and Forest Information  (Benoit et al. 2015a,b). The strong advantages of the GEOCUBE over commercial data loggers reside in its light weight, low-cost and low-energy consumption making it suitable for rapid deployment in cold, harsh and hazardous terrain. Being the first polar experiment C The Authors 2016. Published by Oxford University Press on behalf of The Royal Astronomical Society.
Note that, regardless of the source of seismic noise, extraction of low-frequency (LF) content data below 1 Hz is hampered by the short-period characteristics of the seismic sensor used here. To identify local seismic events we computed the ratio of a short-term window average over a long-term window average (STA/LTA; Allen 1978). We used here window lengths of 1 s and 40 s for the STA and LTA, respectively. Subsequently, seismic signals with longest duration (>15 s) were used as templates in a matched filter in order to detect repeating seismic events. In this search, only signal templates recorded at site#2 and site#4 generated successful matches, possibly due to their signal-to-noise ratios higher than for the other two sites. All seismic signals with correlation coefficient r > 0. 4 for at least one station were kept at first and were then visually inspected in their bandpass filtered and original raw forms.
Our tidally induced GPS-measured vertical displacements lag the CATS02. 01 model by about 1 hr. The favoured cause for such a large time lag is that the CATS02. 01 model may lack assimilated local subice shelf bathymetry information in this particular region together with the non-consideration of nonlinear interactions between ice shelf and tide at the ice shelf shear margins  (Padman et al. 2002). Assimilating such GPS data from a rarely visited ice shelf would improve the spatial coverage of the CATS02. 01 tide model.
A C K N O W L E D G E M E N T S DL acknowledges the financial support of the Belgian Science Policy (BELSPO BR/132/PI/SMEAIS). We are grateful to the International Polar Foundation for the logistic and field support during the BELARE expedition 2013-2014. We thank IGN for the long-term support of the development of the GEOCUBE. We are also grateful for constant development of the following open-source software packages used in this study: Python (python. org), Matplotlib  (Hunter 2007) , Obpsy  (Beyreuther et al. 2010)  and QGIS (qgis. org). We also benefited from the Quantarctica database distributed by the Norwegian Polar Institute (quantarctica. org), from the CATS02. 01 model  (Padman et al. 2002)  distributed via the Tide Model Drive software written by L. Erofeeva and from the SYOWA tide gauge data available from the Intergovernmental Oceanographic Commission archive database (http://www.
Insurance Loss Data Insurance data on losses to residential buildings were provided by the German insurance association (Gesamtverband der Deutschen Versicherungswirtschaft e. V. , GDV). These comprise daily data on administrative district level, with areas ranging from about 40 km² for urban municipalities ("Kreisfreie Städte") to about 3000 km² for rural districts ("Landkreise"). In contrast to pointwise measurements from meteorological stations, the available insurance data represent measurements with an area-wide coverage of windstorm and thunderstorm losses making it most valuable for various weather impact studies. The data however contains some limitations and uncertainties that need to be kept in mind. Uncertainties in daily losses arise from the fact that the exact time of loss occurrence is indistinct in some cases, especially if an event has occurred at night.