dataesr/scientific-paragraphs-categorization huggingface dataset:
It has long been recognised that planning future observations and developing methods to analyse CMB data sets both require realistic models and simulations of the sky emission and of its observations  (Gawiser et al. , 1998; Bouchet & Gispert, 1999; Giardino et al. , 2002; de Oliveira-Costa et al. , 2008; Sehgal et al. , 2007 Sehgal et al. , , 2010)). For the preparation of the analysis of CMB data sets such as those of the Planck mission, it has proven useful to put together a model of sky emission, the Planck Sky Model (PSM), which can be used to predict or simulate sky emission for various assumptions and various parameter sets. The pre-launch version of this model, based on publicly available data sets existing before Planck observations (see  Section 2. 3) , is used in particular as a simulation tool in the context of Planck.
The PSM can also be used to generate model data sets similar to those which could be produced by an instrument. Basic 'sky observation' tools allow for the integration of the sky emission in frequency bands, smoothing with instrumental beams, and observing along scans. A comparison of 'observed' sky predictions with actual data being collected by upcoming instruments allows for real-time assessment of the proper operation of experiments. The sky simulation tool implemented with the PSM was originally developed for investigating component separation methods, building on early work by  Bouchet & Gispert (1999)  for the Phase A study of the Planck space mission. Sky observations targeting the measurement of CMB intensity and polarisation anisotropies in fact contain a superposition of emission from several other astrophysical sources: the diffuse interstellar medium of our Galaxy; radiogalaxies and infrared galaxies; the Sunyaev Zeldovich (SZ) effect from the hot intra-cluster electron gas in massive clusters of galaxies.
Simulations Simulations generated with our model are random (or partially random) realisations. Each time a simulation is generated, a new sky is produced. It is possible however to re-generate exactly the same sky by rerunning the same version of the PSM, with the same sky model options and the same simulation seed (for random number generation). For given cosmological parameters, the power spectrum of the CMB can be predicted using, e. g. , the CAMB software 4   (Lewis, Challinor, & Lasenby, 2000) , so that a plausible CMB sky can be simulated by drawing at random the a m s of a simulated CMB, according to normal distributions with variance C. An interface to the CLASS 5  software  (Blas, Lesgourgues, & Tram, 2011)  is also implemented in the PSM. Specific realisations of sky emission can be produced for other components as well.
Use of the PSM in the Planck collaboration The PSM is the standard sky modelling tool used for simulations in the Planck collaboration. Maps and catalogues obtained from the PSM are either used directly in component separation separation challenges  (Leach et al. , 2008) , or used as inputs to the Level-S software for simulating Planck timelines  (Reinecke et al. , 2006). The PSM software package includes an interface with descriptions of the Planck HFI and LFI instruments for the generation of band-integrated sky maps, and the calculation of color-correction coefficients.
The HEALPix sky pixelisation scheme  (Górski et al. , 2005) , in Galactic coordinates, is used in the current implementation. All the input parameters are passed using a single configuration file, which is read upon execution of the code. This configuration file is stored with the outputs of the simulation for traceability of the simulation. The software consistently uses one single master seed for the generation of all the necessary chains of random numbers, which guarantees the reproducibility of a set of simulations, and the independence of the various random numbers used for the generation of different components of sky emission and instrumental noise. The simulations can be generated at various resolutions, for various values of HEALPix pixel size, and for various values of maximum harmonic mode number max.
In principle, there is no limitation to the resolution (which can be set to 0), or to the HEALPix pixel size (which is limited only by the HEALPix software itself). The maximum CMB harmonic mode is set by the CAMB (or CLASS) software (reference CMB power spectra have been precomputed for max = 10, 000). In practice, the model is constrained by observational data on scales of 6 to 1 • for intensity, and few degrees for polarisation, depending on the component considered. The accuracy of extrapolations at smaller scales are expected to grow worse with increased resolutions, and the model is expected to be useful down to the resolution of the Planck HFI (about 4 resolution). Test cases have been run up to HEALPix N side =4096, and max = 6000. Technical information about the model, released software, data sets and documentation can be obtained from the PSM web site.
Step 3: observations Finally, sky emission maps generated in Step 2 can be 'observed' by simple model instruments. Each channel of the instrument is defined with a frequency band, a beam, polarisation properties, an observing scheme, noise properties, and a format for the generated data sets. Although the generation of instrumental noise excludes at the present slow drifts correlated between detectors, which generate signals that interfere with component separation  (Delabrouille, Patanchon, & Audit, 2002; Patanchon et al. , 2008) , it is possible to include the generation of such effects in future developments of the code. The level-S software  (Reinecke et al. , 2006)  can also be used as a means for sophisticated instrument simulation.
For a standard cosmology (ignoring such effects as, e. g. , cosmological birefringence), the only non-vanishing terms in the power spectrum are C T T , C T E , C EE , C BB , the other two (C T B and C BB ) vanishing for parity reasons. Fast and accurate software is available to compute these power spectra for a given cosmological model, and to randomly generate CMB temperature and polarisation maps for any given such spectrum: in the current version, interfaces to the CAMB  (Lewis, Challinor, & Lasenby, 2000)  and CLASS  (Blas, Lesgourgues, & Tram, 2011)  software are implemented to compute CMB power spectra for a given cosmological model. Alternatively, a default CMB power spectrum can be used, which matches current observational data. Fig. 2  displays the power Fig. 3. CMB temperature and polarisation anisotropy predictions as generated for a 1 • beam, N side = 256, in Galactic coordinates.
9 GB of RAM, on an ordinary PC configuration. Figure  7  shows that the average angular power spectra recovered from 1000 realisations of lensed CMB maps (N side = 1024) are consistent with the corresponding theoretical ones. This agreement between our numerical power spectra estimates and the CAMB predictions is both a validation of our numerical method, and of the CAMB estimates based on partially re-summed perturbative calculations. The accuracy of the lensing simulation is set by default in the current implementation, and is not a parameter of the software. If necessary it can be improved by increasing both the oversampling factor and the convolution length, at the expense of extra memory consumption and CPU time. Since the full precomputation of the window function at each node in the spatial domain requires storage of large amount of real numbers ([2K +1] 2 per pixel in 2-d), we have used a tensor product form for the multivariate window function (the default method within the NFFT library), which requires only unidimensional precomputations.
A fraction of simulated clusters can be replaced by real clusters drawn from the ROSAT all-sky or the SDSS catalogue. Alternatively, we can resort to N-body+hydrodynamical simulations that also contain the SZ emission from the web of intergalactic hot gas. Most radio sources in the model are real, taken from the relatively deep all-sky low-frequency catalogues. Each source has its own spectral properties, either determined directly from multifrequency data or randomly extracted from the observed distributions of spectral indices. Extrapolations to higher frequencies are made using different spectral indices for different frequency intervals, in order to ensure consistency with multi-frequency source counts. This approach turned out to be remarkably successful in predicting the counts that have been later measured by Planck.
The resulting power spectrum of CIB fluctuations turns out to be quite close to that measured by Planck Collaboration (Planck Collaboration XVIII, 2011). The mean polarisation degree of individual infrared galaxies can be adjusted as an input parameter, the default value being 1%. The polarisation angle is randomly chosen from a uniform distribution. On the whole, these simulations should provide a useful tool for several purposes: to test data analysis pipelines for CMB experiments, identify the most convenient areas of the sky for specific purposes (i. e. , areas least contaminated by Galactic emissions), identify the optimal set of frequency channels for CMB temperature and polarisation experiments or for other studies (i. e. , for studies of the cosmic infrared background), predict the levels of confusion noise, including the effect of clustering for a given frequency and angular resolution, and much more.
Acknowledgements We wish to thank our colleagues from the  Planck Collaboration , and in particular members of the component separation working group ( WG2 ), for useful suggestions and discussions, as well as for beta-testing the successive versions of the software. We thank  Karim Benabed ,  Frédéric Guilloux ,  Frode Hansen  and  Benjamin Wandelt  for useful discussions concerning some of the CMB models. Francois Boulanger ,  Rodney Davies  and Francois-Xavier Désert have provided expertise for developing the model of Galactic emission used in this work. We thank  Francesca Perrotta ,  Grazia Umana  and  Stephen Serjeant  for useful discussions concerning the point source model,  Benjamin Walter  for his help in the editing of the present paper,  Hans-Kristian Eriksen ,  Torsten Ensslin ,  Andrew Jaffe ,  Lloyd Knox  and  Douglas Scott  for useful 9 http://www.
Acknowledgments: We greatly acknowledge the farmers and administrators that participated to this research for their knowledge and time;  Christophe Le Page ,  Pongchai Dumrongrojwatthana ,  Guy Trébuil  and and  François Bousquet  for their contribution to the research process; and two anonymous reviewers for their insights and comments on the initial version of the manuscript. We thank the  CGIAR Challenge Program on Water and Food  and the  ASIA-ITC  initiative of the  European Union  for their financial support.
(Ice2sea contribution number  153 ), and the  Nordic Centre of Excellence SVALI , " Stability and Variations of Arctic Land Ice ", funded by the  Nordic Top-level Research Initiative  (TRI, SVALI contribution number  23 ). R. Greve and H. Seddik were supported by a  Grant-in-Aid for Scientific Research  A (no. 22244058 ) from the  Japan Society for the Promotion of Science (JSPS). Computations presented in this paper were performed on the Lindgren cluster in a framework of the  PRACE - DECI7  project  Elmer/Ice. Visits of  F. Gillet-Chaulet ,  L. Favier  and  B. de Fleurian  at  CSC  were carried out under the HPC- Europa2  project (project number  228398 ) with the support of the  European Commission -Capacities Area -Research Infrastructures.
5, 2012). However, bycatch mortality continues, and could possibly increase with the higher catch limit. And some studies suggest that there may have been a transfer of the bycatch mortality to non-US fleets as a result of strict regulations in the US fisheries  [24]. Further reductions of loggerhead takes in the longline fishery may be achieved if longline fisheries target swordfish in places and during times when loggerhead turtles are not occupying the same habitat as swordfish  [23] [24] [25] [26] [27] [28]. To achieve this will require a better understanding of the pelagic habitats of swordfish and loggerhead turtles. The pelagic habitat of loggerhead turtles in the North Pacific has been fairly well described as a result of considerable electronic tracking  [10, [29] [30] [31] [32]. However, all these studies rely on the same extensive dataset.
Tracking data were collected through the ARGOS system by the NOAA PIFSC, Marine Turtle Research Program, Honolulu, Hawaii. To filter out Argos location errors, all tracks were processed the same way as in  [34]. First, all locations resulting from velocities greater than 10 km/h were removed. Then, to remove additional artificial ''spikes'' from the data, an Epanechnikov filter  [35]  was applied with a 2-day window, centered on each individual location, using the lpepa function of the lpridge package in the R environment  [36]. The points providing the 5% largest differences between filtered and observed locations were removed. The original track without these extreme locations was then resampled from a few locations a day (average number of transmissions = 2. 6/day) to a location every 3 hours.
Ethics Statement Tagging and release of turtles from the Hawaii-based longline vessels were done by shipboard observers working with the NOAA/NMFS Observer Program under the U. S. ESA permit #1190. All other animal care and permitting needs for this study were accomplished by the Port of Nagoya Public Aquarium, in full compliance with the requirements and approval of the government of Japan, of which the Aquarium is an entity. Permission to use this data was granted by Dr. Makoto Soichi under a partnership between the Aquarium and the Pacific Islands Fisheries Science Center. All permissions to release the turtles in international waters and Japanese waters were obtained. Size, temperature preference, and swimming speed. The temperature preference and swimming speed of animals were estimated from the tracks using 0.
1u-weekly Pathfinder-GAC satellite SST data (http://www. nodc. noaa. gov/SatelliteData/ pathfinder4km/), provided by the NOAA-Coastwatch program, and filtered 1u61u65d ocean surface currents data, downloaded from the NOAA-OSCAR website (http://www. oscar. noaa. gov). Both data sets were chosen for their coverage of the entire tracking period (1997 to 2008). Values were extracted along each processed track using the Generic Mapping Tools (http://gmt. soest. hawaii. edu/). The change in curved carapace length (CCL) along the tracks was estimated using recently published results on loggerhead turtles growth rates  [14]  to account for the change in size, especially during protracted tracking periods: growth rate (cm/yr CCL) = 210. 6 * log10(CCL) +21. 5. Since turtle sizes were measured in straight carapace length (SCL) on release, CCL was converted to SCL using the relationship in  [11] : SCL = 0.
7 ). Some turtles also went farther east than the simulated ones. As suggested by Fig. 5 , even the smallest turtles in our dataset exhibited some active movement. Fig. 11  illustrates to what extent this affects the animals' movements for the May 2005 release. When combining advection by ocean currents with the directed movements of the animals (Fig. 11B ), the predicted turtle density after a year matched the observed turtle locations significantly better than when pure drift only is considered (Fig. 11A ) and the concentrations of turtles were generally better represented.
Discussion Knowledge about the biology and ecology of juvenile loggerhead turtles is extremely limited based on the challenge of sampling such populations in the open ocean. In this context, the various deployments and tracking of such a significant number of juveniles provided the most comprehensive dataset in the Pacific Ocean, allowing us to investigate this poorly described life stage. However, some caution has to be taken since the turtles from the Japanese releases were reared in captivity. We believe they constitute an acceptable proxy to infer wild behaviors, assuming that reared turtles possess some innate sense from birth. No study to date has observed obvious oddities in migration or swimming behaviors of captive-reared turtles  [10, 21, 25, [29] [30] [31] [32] 50 ]. Never-  theless, further simulations comparing the habitat and behavior of wild and reared turtles with the same physical forcing would be valuable to confirm the results described in the present study.
Temperature In ectothermic species, colder optimal temperatures and wider temperature ranges should be expected for larger individuals, since their larger size should increase their internal body temperature by reducing heat diffusivity  [52]. The range of surface temperatures experienced by the bigger turtles in our dataset (SCL. 50 cm) was larger than that experienced by smaller turtles (SCL,35 cm, Fig. 3 ), as would be expected, but the smaller turtles tended to be in colder surface waters than the bigger ones (Fig. 3 ), which is surprising. However, when considering winter and summer separately, coinciding respectively with weak (mixed-layer deeper than 40m) and strong (mixed layer between 10 and 40 m deep) stratification of the water column, small and large turtles remained in the same narrow range of temperature (16.
Movements A more quantitative way to measure the accuracy of the match between observed and predicted movements needs to be developed. For example, a tool calculating the values of predicted habitat index or density at each location along the observed tracks would help evaluate the performance of each simulation. Nonetheless, the present study suggests that even the smallest turtles in our data set (25 cm SCL) exhibit some level of active swimming, with mean swimming speeds around 0. 6 km/h. We also used the AOML drifter database (http://www. aoml. noaa. gov/phod/dac/dacdata. php) to select drifter tracks for comparison with the turtles. The same analysis was performed on the drifter tracks to remove currents speed and a Kolmogorov-Smirnov test was performed to compare the turtles and drifters' ''swimming'' speeds.
No particle reached Baja California within 5 years in their simulations. Sixty-five of the turtles in our dataset were released off Japan, only 16 of which had track durations of at least 1 year (with SCL at the time of release between 24 and 65 cm), and one of which had a track duration of more than 2 years. At one year after release, 62. 5% of those 16 turtles were between 180 and 200uE vs. only 46. 6% of the simulated hatchlings in  [70]  and 6. 3% were between 200 and 220uE vs. 0. 2%. Only one turtle (SCL = 36. 5 cm) in our dataset, which was released in the Central Pacific at 176. 6uE longitude, reached the coast of Baja California after 4. 9 years (Fig. 1B ). We chose the November 2004 and the May 2005 releases to illustrate our modeling approach because their ranges of sizes were small enough to reasonably treat all the turtles released on each day as one single age cohort.
Nevertheless, the model still needs to be tested for other release experiments, especially with larger individuals. In that case, one issue will be to differentiate between feeding behavior and spawning migrations. A version of SEAPODYM that allows tracking data assimilation is being developed, which could help estimate more precisely the feeding habitat parameters. For reproductive migrations however, temperature and availability of prey would not be the only factors affecting the large-scale movements of loggerhead turtles. This modeling framework could be easily used to introduce and test various migration hypotheses, e. g. based on magnetic fields  [71, 72]. Twenty-six turtles of our dataset, with SCL ranging between 35 and 83 cm, exhibited a net displacement of at least 1 degree westward between the first and last locations of their tracks while all others showed a net eastward displacement.
However, our model does not reproduce those westward movements. This suggests that temperature and availability of prey are not the only factors affecting the large-scale movements of loggerhead turtles. Juvenile and sub-adult turtles spend decades in the open ocean undertaking foraging migrations. Those observed westward movements might be used to offset eastward transport and constitute retention behavior. The oceanic stage of loggerhead turtles has been estimated to last several decades, while our longest track lasted about 3. 5 years. Until tracking capabilities allow for datasets encompassing the entire oceanic stage, understanding the timing of the east-west dynamics of juvenile loggerheads will remain challenging. Nevertheless, confronting these individual tracks to dynamic habitat and movement models already reveals a promising development with direct potential applications that could be rapidly used for assisting in the conservation of these endangered species.
Acknowledgments The authors would like to acknowledge the shipboard observers working with the  NMFS Observer Program  who deployed the transmitters on the longline bycatch turtles, the personnel of the Port of  Nagoya Public Aquarium  and  Marc Rice  and students of the  Hawaii Preparatory academy. The authors wish to thank the  Group Mission Mercator Coriolis  and the GLORYS members (MERCATOR-OCEAN,  INSU-CNRS and the team ''DRAKKAR'')  for providing the GLORYS1V1 reanalysis. We also thank the  Ocean Productivity team  for providing us the SeaWiFSderived primary production and  Beatriz Calmettes , from  CLS , who formatted the forcing data for the model.
A. Quantum-mechanical calculation In the present work, the biomolecule is described via its molecular orbitals by employing the quantum chemical GAUSSIAN 03 program. Briefly, let us note that the target wave functions were computed at the Hartree-Fock level optimized at the MP2/6-31G(d) computational level, i. e. , by including correlation calculations at the second order of perturbation theory MP2 and by using GAUSSIAN-type orbitals added to a double-zeta valence shell and polarization orbitals on nonhydrogen atoms. Total-energy calculations were then performed in the gas phase with the GAUSSIAN 09 software at the RHF/3-21G level of theory. Furthermore, the ionization potentials also calculated at the RHF/3-21G level have shown a very good agreement with the experiments  [30]. The effective number of electrons relative to any atomic component of each molecular orbital was derived from a standard Mulliken population analysis  [31].
ACKNOWLEDGMENTS This work was supported by a  Grant-in-Aid for Scientific Research  (B), Grant No. 24340096 , from the  Japan Society for the Promotion of Science. Also, this work has been developed as part of the activities planned in the  Programme de Coopération PICS 5921  (THEOS) as well as the PEPS-PTI 2013 (ARISTOTE) of the  Centre National de la Recherche Scientifique , the project PICT 2145 of the  Agencia Nacional de Promoción Científica y Tecnológica  and the Projects No. PIP 1026  and No. PIP 0033  from  CONICET.
Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5. 1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover.