dataesr/scientific-paragraphs-categorization huggingface dataset:
As part of the National Large Predator Monitoring Programs, wardens from the State Nature Inspectorate in Norway and from the Environmental Protection Agency in Sweden searched for natal dens in the study population each winter and spring, to obtain a minimum count of the number of reproductive females. Then, based on the assumption that the proportion of reproductive females in the population stays constant, the average count in a window of three years was extrapolated to a minimum population size estimate  (Landa et al. 1998 , Brøseth et al. 2010 ). Hence, the estimates have been based on no formal treatment of the probability to detect a wolverine den in the study area, and the proportion of undetected dens is unknown. Throughout the period 1996-2013, each time a wolverine den was found, its coordinates were included in a national database (< www.
DNA sampling and genotyping Starting in 2002, a protocol of non-invasive genetic sampling, based on scat collection, has been implemented in Norway and Sweden, south of the E14 highway that crosses the whole Scandinavian Peninsula from the North Atlantic Ocean to the Baltic Sea (Fig. 2 ). A similar protocol has been progressively employed also in the northern part of the peninsula after 2008, but less systematically in Sweden than in Norway. Given the shorter time series available and the spatio-temporal heterogeneity in sampling effort, we decided not to use data from the northern portion of the study area for this study, but limited it to the wolverines detected south of the E14 highway during the period 2002-2013. This resulted in a dataset of 3652 genetic samples, belonging to 619 individual wolverines.
Scat samples were collected on snow along wolverine tracks, during a three month period from mid-February to mid-May, after the end of the hunting season and before cubs of the year started accompanying their mother away from the den. This ensured that all collected scats were from individuals one year old or older. In Norway, all the sampling  tissue samples  (Hedmark et al. 2004 ). More than 200 singlelocus genotypes were tested, and three replicates were always sufficient for deriving the correct genotype. The same rigid use of predefined criteria for accepting single-locus consensus genotypes in this study suggests that most errors -if not all -were eliminated from the data set prior to formal analysis. As an additional quality control, a quality index was calculated for individuals that were only represented with one single sample  (Miquel et al.
2006 ). All samples with QI  0. 8 were discarded from the data set. The resulting annual success rate of the genetic identification protocol was of 61% (53-72%), which is comparable to what was obtained in a pilot project on non-invasive genotyping of Scandinavian wolverines  (Hedmark et al. 2004). For sex determination, two Y-chromosome specific markers that had been validated by scat sampling from 16 radio-marked individuals (1 male, 15 females) were used (DBY3Ggu, DBY7Ggu;  Hedmark et al. 2004). Each of the Y-linked markers were amplified twice, leading to four replicates for all scat samples with respect to sex determination. Over the years, many samples have been collected from a large proportion of the individuals. This has provided an extensive database of individuals of known sex, offering an additional opportunity to assess the reliability of the sex markers.
Capture-recapture modeling To assess if data sharing provides analytical benefits for the demographic monitoring of trans-boundary populations, we set up two alternative capture-recapture scenarios, one resembling two isolated national monitoring programs effort was recorded using a global positioning system (GPS) track-log. The total yearly effort was on average around 25 000 km per year, most of which was driven by wardens on snow scooters. No comparable structured record of sampling effort was available from the Swedish monitoring system. After data collection, DNA material was extracted and amplified from all collected scats. Over the years, the DNA extraction and microsatellite genotyping protocols have been modified; from manual to automated DNA extraction and by replacing singleplex PCR amplification with multiplex PCR.
We used a similar approach for our case study. We compiled a dataset of about 12 000 locations from 26 wolverines, captured and fitted with GPS-collars in the regions of Finnmark, Troms and Trøndelag in central-northern Norway, between 2010 and 2013. We selected locations referring to the period February-May, and calculated the average GPS location for each individual. Then we calculated the distance between this center point and all the other locations, and produced a probability distribution, estimating the proportion of time each individual was expected to spend farther than a given distance from its home range center (Fig. 3a, c ). As the probability distribution function was significantly different for males and females, we produced a different curve for the two sexes. This allowed us to estimate the proportion of time each individual wolverine sampled by DNA was expected to spend in Norway and Sweden, based on the distance between the geometric center of its DNA sampling locations and the national border (Fig.
Moreover, we needed to account for the potential presence of individual heterogeneity in detection probability, which can produce biased estimates of abundance when not properly accounted for  (Hwang and Huggins 2005, Cubaynes et al. 2010). Therefore, we used the general framework of multi-event models  (Pradel 2005)  in program E-SURGE  (Choquet et al. 2005)  to integrate a two-site capture-recapture model with live detections and dead recovery  (Burnham 1993) , and a mixture model incorporating individual heterogeneity in detection probability  (Pledger et al. 2003 , Pradel 2009 ). In the case of the SHARE design, all wolverines were potentially observable while alive, irrespective of their transition across the Norway-Sweden border, as a result of the data-sharing between the two monitoring programs.
Therefore, the resulting model structure comprised seven possible states. Individuals alive in Norway could have either high or low detectability, and the same classification was used for wolverines in Sweden. Additionally, we included a specific state for wolverines legally harvested in Norway, one for the individuals dying from other causes, and one absorbing state containing all individuals dead in previous years. We did not include in the model the probability of emigration outside the study area across the E14 highway, as our data showed that such probability could be reasonably disregarded. Out of the 683 wolverines detected within 200 km north and south from the E14, only about 1% was detected on both sides of the highway during the whole 12-year monitoring period. Detection probability for legally shot wolverines was set to one, as all of them were reported to the authorities.
We performed a goodness of fit test in program U-CARE  (Choquet et al. 2009) , which revealed no excess of transients and no trap-dependence. For the NOSHARE design, the detection matrix was modified to take into account that Norwegian wolverines moving to Sweden became unobservable and vice versa. This corresponded to the temporary-emigration model of  Kendall and Nichols (2002) , which allows the production of unbiased estimates of survival and transition probabilities, under Markovian (i. e. non-random) temporary emigration  (Schaub et al. 2004 ). We used the test 2C. t in program U-CARE  (Choquet et al. 2009 ) to reveal such a possible non-random temporary emigration  (Schaub et al. 2004 ), which was indeed confirmed (z  -2. 343, p  0. 019). This indicated that estimating population size, survival, and transition probabilities from a design with no trans-boundary data sharing required the addition of an unobservable state, as biased parameter estimates would have been likely otherwise  (Schaub et al.
While the den-count monitoring provided an estimated population growth of 138% over the 12-year period, the DNA-based monitoring estimated such growth to be only 72% during the same period. Also, as shown in Fig. 6 , the den-count monitoring produced the image of a population growing at a monitoring scenarios were rather similar, but the ones based on trans-boundary data sharing were more precise (Fig. 4 ). The average coefficients of variation (CV) of population size estimates were 0. 24 and 0. 17 for the NOSHARE and SHARE design, respectively, indicating a 30% increase in precision. Also for the two national estimates, the SHARE design provided more precise estimates. Such reduction of uncertainty was about 16% in Norway (from 0. 21 to 0. 18 CV) and about 48% in Sweden (from 0. 31 to 0. 16), showing that Sweden benefitted the most from the sharing of wolverine DNA data.
It was less obvious that the effort by different national monitoring institutions to set up and maintain a common monitoring program on a transboundary population, with shared protocols and database, could produce large benefits on the amount and quality of the monitoring output. While 88% of European large carnivore populations are trans-boundary in nature  (Linnell et al. 2007b) , most of them are still monitored and managed at a strictly national scale, with poor communication between the bordering monitoring institutions  (Linnell et al. 2007b). The results of our study indicate the potential benefits deriving from a trans-boundary integration of large carnivore monitoring systems and the risks of failing to do so. phase in the monitoring process. A structured count of wolverine reproductive units started in Scandinavia in 1996, so that 25% of known denning sites had already been discovered before the beginning of the DNA sampling in 2002, while an additional 40% were first found and included in the national databases during the first three years after 2002.
Acknowledgments -This study is based on data from the  Norwegian Large Predator Monitoring Programs in Norway and Sweden , financed by the  Directorate for Nature Management in Norway  and the  Environmental Protection Agency in Sweden. The study would not have been possible without the enormous sampling effort by dedicated wardens from  SNO  and  NVV. We thank  Professor Hans Ellegren  and the  DNA laboratory  technicians at the  Dept of Evolutionary Biology, Evolutionary Biology Center, Uppsala, Sweden , for producing the genotyping data that constituted the basis for the individual identification of wolverines. Data analysis was funded by the  Research Council of Norway  and the  Norwegian Environmental Agency.
Materials and Methods The Natural History Museum of National University of San Marcos (MUSM), Lima, Peru, provided all permits for this study and houses permanently the specimens from the Pebas Formation described in this publication  (Credential Number MUSM 2008-8). All data has been collected, prepared and deposited in this public national Peruvian repository by the authors of the article. The current data is based only in fossil specimens, to which it has been assigned a specific catalog number. Specimen numbers are MUSM 1981, MUSM 987, MUSM 900, MUSM 1681, MUSM 2032, MUSM 1988, MUSM 1727, MUSM 1439, MUSM 2407, MUSM 1428, MUSM 1435, MUSM 1440, MUSM 1682, MUSM 1993, MUSM 2470, MUSM 2471, MUSM 2472. All of these Peruvian fossil specimens are completely available in this public repository.
Cranial circumorbital morphospace analysis and phylogenetic mapping In order to understand morphological evolution linked to longirostry, we applied a geometric morphometric approach to analysis of the circumorbital anatomy of 22 species of crocodylians, including extant and extinct species (S1 Appendix). As the circumorbital region has shown only minor intraspecific variation within extant adult crocodylians  [16] , all taxa are represented by one fully adult specimen, each preserving in dorsal view: (1) rostral sutures, (2) orbital shape pattern, and (3) no significant postmortem distortion. Morphometric analyses require complete datasets for all landmarks, thus MUSM 1981 was partially reconstructed from the morphology of the complete left side via geometric reflection  [17] , and included in the analysis.
We selected twenty discrete landmark loci based on their availability, relative coplanarity, and clear demarcation within images (S1 Fig). Two-dimensional landmark digitization, Procrustes superimposition, and principal component analyses (PCA) were performed with the Geomorph package in R software  [18, 19]. The 22 landmark configurations were superimposed and scaled to unit centroid size following the generalized Procrustes method  [20]. The coordinates of the superimposed configurations were later imported into the Euclidean tangent shape space for subsequent statistical analysis  [20]. In order to determine components of shape variation, the 22 principal components from that analysis were obtained by applying a principal component analysis (PCA) on the variance covariance of the projected coordinates.
Systematic paleontology Crocodyliformes  Hay, 1930 [36]  Eusuchia Huxley, 1875  [37]  Crocodylia Gmelin, 1789  [38]  Gavialoidea  Hay, 1930 [36]  Gryposuchinae Vélez-Juarbe et al. , 2007  [39]  Gryposuchus Gürich, 1912  [40]  Gryposuchus pachakamue sp. nov. ZooBank life science identifier (LSID) for species. urn:lsid:zoobank. org:act:6B71903E-9412-44BE-8537-203B07909DEE Etymology. The Pebasian Gryposuchus species is named after the Quechua word "pachakamue", primordial pre-Columbian god and first "storyteller"  [41]  who preserved ancient knowledge about the origin of living things in Amazonia. [28]  and fossil bearing localities (IQ) within the Pebas Formation and the "Uppermost Pebas Formation" is shown. Geological data from  [33]. Diagnosis. Gryposuchus pachakamue is a long-snouted gavialoid crocodylian diagnosed by the following unique combination of characters: 22 maxillary and 22 mandibular teeth; ventral margin of postorbital bar inset from lateral jugal surface; frontoparietal suture between supratemporal fenestrae strongly concavoconvex; splenial with anterior perforation for mandibular ramus of cranial nerve V posterior to symphysis; splenial constricted within symphysis; surangular-dentary suture intersecting external mandibular fenestra at posterodorsal corner; surangular-articular suture bowed strongly laterally within glenoid fossa.
Acknowledgments We thank  A. Balcarcel  and  W. Aguirre  for fossil preparation. We are indebted to  C. de Muizon  and  S. Bailón  (MNHN);  R. Hulbert ,  J. Bloch ,  B. MacFadden , and  K. Krysko  (UF),  C. Mehling  (AMNH);  E. "Dino" Frey  (SMNK), and R. (SMNS) for access to comparative collections;  A. Valdés-Velasquez ,  M. Orliac ,  G. Billet ,  J. Martin ,  R. Lebrun , and  F. Pujos  for invaluable discussions;  V. DeLeon  for providing substantial insights on morphometric analyses and interpretation, and  M. Burga  for assistance in gathering data for this analysis; and  P. Bona ,  C. Brochu ,  S. Jouve ,  T. Scheyer , and  J. Moreno-Bernal  for providing visual material of crocodylians. We are much indebted to  C. Brochu  and  J. Martin  for their constructive critical reviews. This is ISEM publication 2016-023 S.
The study was financially supported by the  Frick Fund, Division of Paleontology, American Museum of Natural History. RS-G benefitted from a  Doctoral grant  of the  Escuela Doctoral Franco-Peruana en Ciencias de la Vida, and from financial support of the Frick Fund  ( American Museum of Natural History ) and the Projects-Sud of the ISEM for visiting collections,  Institut de Recherche pour le Développement , and from  CampusFrance.
The Fast Plasma Investigation (FPI) was developed for flight on the Magnetospheric Multiscale (MMS) mission to measure the differential directional flux of magnetospheric electrons and ions with unprecedented time resolution to resolve kinetic-scale plasma dynamics. This increased resolution has been accomplished by placing four dual 180-degree top hat spectrometers for electrons and four dual 180-degree top hat spectrometers for ions around the periphery of each of four MMS spacecraft. Using electrostatic fieldof-view deflection, the eight spectrometers for each species together provide 4pi-sr field-ofview with, at worst, 11. 25-degree sample spacing. Energy/charge sampling is provided by swept electrostatic energy/charge selection over the range from 10 eV/q to 30000 eV/q. The eight dual spectrometers on each spacecraft are controlled and interrogated by a single block redundant Instrument Data Processing Unit, which in turn interfaces to the observatory's Instrument Suite Central Instrument Data Processor.
The addition of light trap structures near the entrance apertures was deemed desirable but discarded as an acceptable performance trade in light of the additional complexity and mass required for the 64 spectrometers. A photon ray-tracing study was performed for the DES optics design by GSFC and iterated as the design evolved. The same analysis was repeated on the DIS design with assistance from ISAS and tests were performed in the ISAS beam chamber to determine the effectiveness of mitigation strategies. The studies were performed using the commercial "FRED Optical Engineering Software" that models photon transmission through the detailed mechanical design, including surface properties. The top five paths by which photons could reach the detectors were determined, all involved reflections from the ESA top cap.
These functions are completed in real time so the results can be stored in the CIDP Mass Memory Module (MMM) synchronously with their generation. Table  3  provides a summary of the modes and functionality of the FPI IDPU. The IDPU receives data and commands from the ground via the CIDP. The IDPU directly controls and collects data from and the four DES and DIS units. In response to the promptings of the IDPU, each dual spectrometer executes ESA and deflector voltage stepping, collects data and provides the data in the form of counts per integration interval. The Burst Quality Indices (BQI) FPI trigger terms are defined as seven weighted sums (three for electrons and four for ions) that are performed in the IDPU over the DES and DIS Burst skymaps at the burst time resolution (30 ms for DES and 150 ms for DIS).
FPI Flight Operations In orbit (Fig. 56 ) FPI operates in several different modes. It operates in Fast Survey mode when the spacecraft is within the Science Region of Interest (ROI), nominally defined as the high altitude half of each orbital period; and operates in Slow Survey mode or one of several calibration modes between L = 7 and the beginning of the next ROI; and operates in a low voltage Engineering Mode below L = 7 to suspend operations when traversing the radiation belts. In Fast Survey mode, all spectrometers are stepped at their fastest rate, with DES running at 30 ms and the DIS at 150 ms for a full Burst sky map at 32 energies and 32 azimuth angles. In this mode, sampling rates are much faster than the spacecraft spin. A Fast Survey data product, reported every 4. 5 sec, is constructed by de-spinning-performed in the IDPUand summing individual Burst sky maps.
A Macro is a group of commands that can be executed rapidly and sequentially with a reliable, fixed delay between commands. The use of RTS and Macros enables FPI to execute the vast array of command sequences necessary to maintain stable. Command macros are generated and managed by means of a suite of Interactive Data Language (IDL) tools designed to facilitate human review and revision. Macros are version-  FPI Reset FPI executes a reset upon exiting the inner magnetosphere, at L = 7, to mitigate corruption of system settings by radiation-related single event upsets. The procedure performs an IDPU soft reset called from the ATS, followed by execution of the FPI Neutral Macro. The FPI operating table chosen is based on the temperature of FPI and is selectable via a parameter in the IDPU reset command.
Ramp to Slow Survey The Ramp to Slow Survey activity configures one quadrant of FPI (one DES and one DIS) to ramp up the MCPs and enter Slow Survey data collection. Though all spectrometers remain powered during Slow Survey, only one of the four dual spectrometers pairs has its high voltage enabled. Fast Survey The Fast Survey activity sets the integration time to 195 µs and 1 ms for DES and DIS, respectively. A snapshot of the instrument configuration (register settings, operating tables, etc. ) is recorded prior to entry to Fast Survey mode. Nominally, an MCP ramp is not required prior to entry to Fast Survey because the system is already at operating voltage following Operating Point Calibration. Operating Point Calibration The detection system operating point calibration activity determines the balance between signal loss and crosstalk between pixels and ideally minimizes both of these values.
MCP Ramp The MCP ramp activity steps the MCP voltage up to full operating voltage over the course of several minutes. During this ramp, counts are collected with an optics dark stepper configuration that eliminates incoming plasma from the aperture. These collected counts then serve as a background noise measurement that, if consistent and significant, can be subtracted from collected science data. Burst Data Calibration Burst data calibration is performed by acquiring burst resolution data for a period of up to three spins (60 s) at a selected point within the ROI of each orbit. This data is used to perform statistical analyses, exposing and quantifying certain calibration errors. The errors are then corrected during ground processing. The particular data to be used are selected by the FPI team and marked for download through a similar interface to that used by the Scientist in the Loop.
FPI Data Processing and Products FPI produces scientific data in the two modes of operation called Fast Survey and Slow Survey. In Slow Survey mode, skymaps are acquired every minute for both DES and for DIS. These data have low relevance for satisfying the primary mission objectives, and they comprise less than ∼ 1 % of the FPI scientific data included in spacecraft telemetry to the ground. These skymaps are the only scientific data produced by the FPI IDPU in the Slow Survey mode of operation. Fast Survey mode is used when the spacecraft are in the science ROI. In Fast Survey mode, the FPI produces two types of skymaps for both the DES and the DIS. Burst skymaps are acquired every 30 ms for the DES and every 150 ms for the DIS. Survey skymaps are an accumulation of burst skymaps, with counts redistributed to 32 sun-referenced angular sectors using the information on rotation phase recorded for each burst skymap.
Survey skymaps are produced nominally every 4. 5 s. Each is a combination of 150 burst skymaps for the DES, or 30 burst skymaps for the DIS. All skymaps are compressed by the IDPU prior to transmittal to the ground. Dependent on the level of complexity of the skymaps and other qualities of the data, the compression may be either lossless, or lossy. Typical compression levels achieved for the skymaps are between a factor of 4 and 5 for Burst and Slow Survey data products and slightly larger than 16 for the Fast Survey data products. The skymaps are decompressed prior to ground processing of the data sets.
On-Board Processing of Scientific Data Because of telemetry limitations and the high volume of data acquired in Fast Survey mode, even with compression, it is not possible with the available downlink capacity to send all burst skymap data to the ground. The entire set of Slow Survey and Fast Survey skymaps are telemetered, but burst skymaps are queued only for intervals identified as highest priority for scientific analysis. Downlink priority is determined by the CIDP with an automated triggering process that relies on trigger data produced by FPI and other instruments. The automated selections are reviewed and can be overridden by scientists on the ground prior to downlink. In Fast Survey mode the FPI IDPU computes two sets of burst trigger quantities; trigger terms (TT; 30 ms cadence) and trigger data numbers (TDN; 10 s cadence).