dataesr/scientific-paragraphs-categorization huggingface dataset:
The ACT and SPT data, when added to the Planck data at 143 GHz, cover more than four orders of magnitude in flux density. Finally, we checked that our counts are in agreement with the Planck Sky Model  (Delabrouille et al. , 2012). Figure  10. Same as Fig. 9 , but on a wider flux density scale and with the addition of ACT  (Marriage et al. , 2011)  and SPT  (Vieira et al. , 2010)  data shown as squares at 143 and 217 GHz, and SCUBA and LABOCA data shown as stars at 353 GHz  (Borys et al. , 2003; Coppin et al. , 2006; Scott et al. , 2006; Beelen et al. , 2008; Weiss et al. , 2009). Notice that we added the model of  Bethermin et al. (2011)  at 500 µm (dash-dot) to comply with the Herschel data taken at that wavelength (and not at 545 GHz). Figure  11. Planck integral number counts (filled circles); dusty (red); synchrotron (blue).
(2009 ), Franceschini et al. (2010) ,  Lacey et al. (2010) ,  Marsden et al. (2011) ,  Wilman et al. (2010), and Rahmati & van Der Werf (2011). A comparison is given with these models in Fig. 12   based on an analysis of all the main source populations at GHz frequencies. It currently provides a good fit to all data on number counts and on other statistics from ∼ 5 GHz up to ∼ 100 GHz. This model adopts a simple power-law, with a very flat spectral index (α ≃ -0. 1), for extrapolating the spectra of the brightest extragalactic sources (essentially "blazar 2  sources") to frequencies above 100 GHz. The  Tucci et al. (2011)  models provide a description of three populations of radio sources: steep-, flat-, and invertedspectrum. The flat-spectrum population is further divided into Flat-Spectrum Radio Quasars (FSRQ), and BL Lacs.
Dusty sources The  Serjeant & Harrison (2005)  model performs reasonably well at 857 GHz, but is lower than our observations at 545 and 353 GHz. The  Bethermin et al. (2011)  model has the same trend -it is compatible with the data at 857 GHz, but is lower than the observations by a factor of about 3 at 353 and 545 GHz. This is likely due to the limits of that model's validity at high flux density (typically above one Jy). For both models, the likely origin of the discrepancy with our new, Planck, high-frequency data is the models' inaccurate description of local SEDs. Since the counts of bright sources at high frequency depend mainly on the SED of low-z, IR galaxies, rather than on cosmological evolution at higher redshifts, any discrepancy with models is telling us more about their accuracy in reproducing the averaged SED of the low-z Universe than about any cosmological evolution.
, 2009)  and in all bands  (Massardi et al. , 2009; de Zotti et al. , 2010) , and finally IRAS 25, 60 and 100 µm results  (Lonsdale & Hacking, 1989; Hacking & Soifer, 1991; Bertin & Dennefeld, 1997). The Spitzer level at 24, 70 and 160 µm comes from counts above 8, 70 and 300 mJy, respectively  (Bethermin et al. , 2010a). We also plot the models of  Serjeant & Harrison (2005)  (based on IRAS and SCUBA 850 µm local colors), of  de Zotti et al. (2005) , of  Bethermin et al. (2011) , and of  Tucci et al. (2011). As expected from our data on number counts discussed above, our current and the early Planck estimates are in good agreement at 100 GHz. Also, the Planck LFI and WMAP estimates agree within the error bars. The Planck contribution is unique in disentangling the dusty from synchrotron sources in the key spectral regime around 300 GHz where the two populations contribute equally to the Euclidean level.
We might instead want to use the luminosity density estimated at 850 µm from previous studies, and correct it to account for the excess observed by Planck. We can thus use:  Luminosity density ρ ν (in units of h L ⊙ Mpc -3 ) of dusty galaxies, derived from the Euclidean level p and scaled to the luminosity density of: SCUBA 850 µm data  (Dunne et al. , 2000; Serjeant & Harrison, 2005 ) (see Eq. 12); or IRAS 60 µm data  (Soifer & Neugebauer, 1991; Bertin & Dennefeld, 1997; Takeuchi et al. , 2003)  (see Eq. 11). Red circles: estimate from Planck for dusty galaxies (this work); green diamonds: compilation from  Takeuchi et al. (2006) ; black dots: model from  Serjeant & Harrison (2005). Values are given in Tab. 9. Our Planck estimates lie in the shaded area. Note that we take h = 0. 71 here. ρ ν = p ν p 850.
(2011)  agrees marginally at 857 GHz but is too low at 545 GHz and at lower frequencies, while the model of  Serjeant & Harrison (2005)  is marginally lower at 857 GHz, fits the data well at 545 GHz, but is too low at 353 GHz. The likely origin of the discrepancies is an inaccurate description of the SEDs for galaxies at low redshift in these models. Indeed a cold dust component, seen e. g. by Planck Collaboration XVI (2011), is rarely included in the models at low redshift. This failure to reproduce high-frequency counts at bright flux density should not have any impact on the predictions at fainter flux densities and higher redshifts, as is shown in the good fit to Herschel counts. Nevertheless it tells us about the ubiquity of cold dust in the local Universe, at least in statistical terms.
We derive a range of values for the local luminosity density for dusty galaxies, based on simple considerations and using the SCUBA 850 µm and IRAS 60 µmluminosity density as a reference. The Planck multi-frequency all-sky survey is very rich dataset, in particular for extragalactic studies (e. g. Negrello et al. 2012). The final Planck catalogue of sources will be based on.
Figure14. Luminosity density ρ ν (in units of h L ⊙ Mpc -3 ) of dusty galaxies, derived from the Euclidean level p and scaled to the luminosity density of: SCUBA 850 µm data (Dunne et al. , 2000; Serjeant & Harrison, 2005 ) (see Eq. 12); or IRAS 60 µm data (Soifer & Neugebauer, 1991; Bertin & Dennefeld, 1997; Takeuchi et al. , 2003)  (see Eq. 11). Red circles: estimate from Planck for dusty galaxies (this work); green diamonds: compilation from Takeuchi et al. (2006) ; black dots: model from Serjeant & Harrison (2005). Values are given in Tab. 9. Our Planck estimates lie in the shaded area. Note that we take h = 0. 71 here.
Acknowledgements. Based on observations obtained with Planck (http://www. esa. int/Planck), an  ESA  science mission with instruments and contributions directly funded by  ESA Member States ,  NASA , and  Canada. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN  and  JA (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
The first technique we use for the final reconstruction of the map is PhotProject. We first remove the 1/f noise (correspond- ing to data with low spatial frequencies or large scale structures in the map) using a high-pass filter. We then use PhotProject to reproject the data on the sky. The high-pass filtering step is optimum for compact sources but can lead to suppression of extended features (corresponding to low spatial frequencies) in extended sources. MADmap (Microwave Anisotropy Dataset mapper) produces maximum likelihood maps from the time ordered data  (Cantalupo et al. 2010). The main assumption here is that the noise is uncorrelated from pixel to pixel. However, one component of the 1/f noise is strongly correlated from pixel to pixel, as it is due to the thermal drift of the bolometres, and thus not treated by MADmap.
Nevertheless, MADmap is more efficient than PhotProject in reconstructing the extended structures within a map. Scanamorphos is another technique specially developed to process scan observations  (Roussel 2012). The particularity of Scanamorphos, compared to MADmap, is that no particular noise model is assumed to deal with the low-frequency noise (the 1/f noise). Indeed Scanamorphos takes advantage of the redundancy in the observations, i. e. , of the fact that a portion of the sky is observed more than once and by more than one bolometre. The two noise sources contributing to the low-frequency noise are inferred from the redundancy of the data and removed  (Roussel 2012). The maps are made using the default parameters. We add the minimap option when reducing data with a field size of the order of 8.
Determination of unc sum : For each pixel there is a contribution from the background noise to the total measured flux density. This error, σ sky , is the same for a pixel in the source aperture as well as in the background aperture, repeated N ap times here. The error, σ sky , is the standard deviation of all pixels in the background aperture. The final uncertainty, unc sum , is then: unc sum = N ap σ sky (2) Determination of unc int : For each pixel there is an underlying uncertainty for the flux density value in the pixel, σ int,i , and is independent from pixel to pixel. This uncertainty arises from the data reduction step when the flux density for each pixel is computed. A map of these uncertainties is produced during the data reduction process. The uncertainty, unc int , is then derived by adding quadratically all of the errors in the considered pixels : unc int = N ap i=0 σ 2 int,i (3) Note that the assumption of pixel-to-pixel independent uncertainty is not applicable for PACS maps and this can result in an underestimation of unc int.
This will affect the determination of the flux density for each pixel summed in the aperture : unc bg = N ap unc bg level (6) The uncertainty due to background noise in the source aperture, unc source , is determined the same way as the PACS unc ap since it is the uncertainty arising from summing the pixels in a given aperture : unc source = N ap σ sky (7) The uncertainty arising from the underlying uncertainties of the flux density value in each pixel is computed the same way as for PACS. Here again, this uncertainty arises from the data reduction step when the flux density for each pixel is computed, and the pipeline produces the corresponding error map : The total uncertainty coming from the determination of the flux density for an extended source, is then : unc int = N ap i=0 σ 2 int,i (8) unc f lux = unc 2 bg + unc 2 source + unc 2 int + unc 2 beam (9) For both types of sources, we also add calibration uncertainties to unc f lux to get the final total uncertainty.
MIPS photometry The table of the available MIPS data for the DGS is given in  Madden et al. (2013)  and  Bendo et al. (2012)  who give a detailed description of the photometry for total galaxy flux densities. Of the DGS sample, 34 galaxies have been observed by MIPS in the considered bands. Bendo et al. (2012)  MIPS flux densities compare well with previously published MIPS samples containing a subset of the DGS galaxies  (Dale et al. 2007; Engelbracht et al. 2008 ). Therefore we are confident about the reliability of these results and will use them to perform the comparison with our PACS flux densities.
The very faint and discrepant galaxies at 70 µm are HS1222+3741 (ratio of 0. 40) and Tol1214-277 (ratio of 0. 24). For HS1222+3741, the MIPS image contains some bright pixels near the edge of the photometry aperture used for MIPS, and this may have driven the 70 µm MIPS flux density up. For Tol1214-277, a nearby source is present in the MIPS data and, although its contribution has been subtracted when computing the MIPS 70 µm flux, some contribution from this source may still be present. Additionally, measuring accurate flux densities at ≤ 50 mJy in both MIPS and PACS data is difficult and may have led to the discrepancies. 9 The corresponding documentation for PACS colour corrections is available at http://herschel. esac. esa. int/twiki/pub/Public/Pacs CalibrationWeb/cc report v1. pdf 10 The MIPS Instrument Handbook is available at http://irsa.
Acknowledgements. The authors would like to thank the referee,  Simone Bianchi , for his valuable comments that helped improve the quality of this paper. ARR is supported by a  CFR  grant from the  AIM laboratory (Saclay, France). This research was, in part, made possible through the financial support of the  Agence Nationale de la Recherche (ANR)  through the  programme SYMPATICO (Program Blanc Projet   ANR-11-BS56-0023 ). IDL is a postdoctoral researcher of the  FWO-Vlaanderen (Belgium). PACS has been developed by  MPE (Germany) ;  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAF-IFSI/OAA/OAP/OAT ,  LENS ,  SISSA (Italy) ;  IAC (Spain). This development has been supported by  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI/INAF (Italy) , and  CICYT/MCYT (Spain).
Relationship between Wt and reduced heat flow In general we assume that the mantle and crust heat flux is proportional to the average surface heat flux. Pollack and Chapman (1977)  argued that mantle heat flux represents 40% of the regional average surface heat flux. Despite the fact that their measures were based on a small dataset, we consider here that they are valid up to a minimum scale of about 300 km  (Mareschal and Jaupart 2004). Average heat flow data suggest an empirical relationship of the form: Q ¼ Qo þ b⋅ H ð24Þ where Q and H represent average heat flux across the designated area and heat production, Qo is reduced heat flow and b represents the thickness of a shallow layer enriched by radiogenic elements. Eq. (  24 ) reflects changes in average heat flux on a larger scale (> 200 km) and is based on a very large dataset.
Study of the equilibria The basic reproductive number of a disease, R 0 , is the expected number of secondary cases caused by one infected individual introduced into a susceptible population  [40]. A disease is expected to spread only if R 0 is greater than unity. We calculated R 0 in our model using the Next Generation Approach  [40, 41]. Details of the calculations are presented in the Supporting Text S2. The system of differential equations (  1 ) is then solved numerically, using the deSolve package  [42]  in R  [43]. Deter-ministic simulations are run on a time long enough to ensure that equilibrium states are reached (typically t~300 years with our parameters). When it is possible to find analytical solutions for the equilibria (for example with system (S1. 2) without fleas, in the Supporting Text S1), we can check the accuracy of the numerical integrations of the model.
Stochastic analyses To assess the effect of population structure on the persistence of the disease, we use a stochastic version of our model without resistant rats (system (S1. 1) in the Supporting Text S1), based on the Gillespie algorithm  [47]  that is implemented in the GillespieSSA R package  [48]. It simulates a Markov stochastic process in continuous time and with discrete state values. We ran simulations both with and without metapopulation structure, in order to compare the persistence of plague (a hundred replications for each set of parameters). The number of simulations where susceptible rats S or infectious rats I persist was recorded over time, to obtain an estimation of the probability of extinction of rats S and I through time. The scripts of the simulations (deterministic and stochastic) are deposited in the Dryad Repository: http://dx.
As for data collection methods, legal and literature research, document analysis, informal interviews with experts and informed participants (such as judges, ICT and administrative personnel and lawyers etc. ) as well as on-site observations have been carried out by the authors. In particular, the direct involvement of two of the authors in the implementation of TéléRecours provided the rare occasion for an in-depth, continuous observation and did not just limit the research to ex-post data collection. 19 At the same time, the more detached position of the third author and his research experiences in the e-justice field reduces the risks of bias due to the personal involvement of the other authors in the phenomenon under observation. For the purpose of this paper, data have been collected systematically over a period of time that extended from February 2009 to December 2010, and updated whenever possible to August 2012.
Technically, it is a webbased service retrieving data in the courts' local Oracle databases, using Microsoft Visual Studio. NET technology. Users have voiced their strong satisfaction with Sagace: the service was used in 95% of cases during the first year, each case file being visited on 2 or 3 occasions. By the end of 2006, this trend was confirmed, with 920,000 visits during the last two years, a 58% increase in traffic, 156,000 cases viewed and the average number of visits per case having grown from 2 to 6. 63.
12 ± 0. 06 Pg C yr -1. There is insufficient data available to provide a quantitative assessment of interannual variability or long-term trends in CO 2 flux in the Arctic, although the dynamic nature of the region suggests that the former would be relatively large. Speculation over the future trend of the carbon flux in this region has suggested that the net CO 2 sea-air flux in the Arctic will grow more strongly negatively associated with further sea-ice loss. It is important to note that previous synthesis and model studies of the Arctic Ocean CO 2 flux were based on data collected prior to the major summertime seaice loss event in 2007  (Bates and Mathis, 2009 ) -a transformation in the sea-ice extent of the Arctic that has continued to the present day. More recent surveys conducted suggest that there may not have been an increase in CO 2 uptake since the 2007 sea-ice loss event  (Cai et al.
Further, it is critical to distinguish whether the significant decadal climate variability in this region is responsible for observed changes, as opposed to long-term trends. McKinley et al. (2011)  addressed these issues by using an updated pCO 2 database  (Takahashi et al. , 2009)  to estimate pCO 2 trends in three North Atlantic basin-scale biomes for a range of time frames between 1981 and 2009. They illustrated that in the subpolar region it takes at least 25 yr for the driving force of ocean carbon uptake to be predominantly anthropogenic carbon accumulation in the atmosphere, and that the shorter-term changes reported from observations are best interpreted as the result of decadal variability and not by long-term declines in ocean carbon sequestration (e. g. Gruber et al. , 2009). Variability in the subpolar carbon sink is due to the various and opposing influences of the decadal climate variability, with the North Atlantic Oscillation (NAO) being the dominant mode of climate variability here.
Methods Consistent with the RECCAP methodology, we use global "Tier 1" methodologies for our primary analysis  (Canadell et al. , 2011). These are sea-air CO 2 fluxes from (1) a sea surface pCO 2 climatology, (2) ocean inversions, (3) atmospheric inversions, and (4) ocean biogeochemical models. Additionally, we use flux estimates based on the gridded product of monthly sea surface observations of f CO 2 from the Surface Ocean CO 2 ATlas (SOCAT) and fluxes estimated at the regional scale based on the pCO 2 database analysis of  McKinley et al. (2011). In the subtropical North Atlantic, we also compare fluxes based on sea surface pCO 2 observations at BATS and at ESTOC. Throughout, when referring to "fluxes", it refers to contemporary fluxes, i. e. the total flux that is the sum of natural fluxes, fluxes resulting from riverine inputs, and the perturbation due to anthropogenic carbon accumulation in the atmosphere  (Gruber et al.
The gas transfer velocity k was calculated using the wind formulation by  Wanninkhof (1992)  with bomb 14 C corrections by  Sweeney et al. (2007). Wind speed data, taken from the 6-hourly CCMP Wind Vector Analysis dataset  (Atlas et al. , 2011) , were provided for the RECCAP project  (Wanninkhof et al. , 2012 ). The solubility s was calculated according to the method presented by  Weiss (1974) , using the in situ temperature and salinity values recorded with each measurement. Atmospheric xCO 2 values were obtained from the reference matrix of GLOBALVIEW (varying over time and latitude; GLOBALVIEW-CO 2 , 2011), regridded and converted into f CO 2 using NCEP/NCAR sea level pressure and sea surface temperatures  (Kalnay et al. , 1996). This resulted in varying atmospheric f CO 2 over time, latitude, and longitude, due to the variability of sea level pressure and sea surface temperature.
f CO 2 was then computed as sea surface pCO 2 minus atmospheric pCO 2. It is worth noting here that the non-ideal behavior of CO 2 gas is corrected for in f CO 2 ; the f CO 2 is generally indistinguishable from the pCO 2 , provided atmospheric f CO 2 is used in the former. Uncertainty is estimated as 50 % of the regional mean flux for the SOCAT fluxes. Additionally, we include an analysis of regional CO 2 fluxes and trends based on the observed in situ pCO 2 database of  Takahashi et al. (2009)  using the method of  McKinley et al. (2011)  adapted to the regions for this analysis (Sect. 2. 3). In our North Subpolar region, we also include pCO 2 calculated from direct observations of DIC, TA, SST, and salinity between Iceland and Newfoundland (SURAT-LANT) of  Metzl et al. (2010). In this approach, in situ observations of surface ocean pCO 2 are collapsed onto a single time series for each region, and then a harmonic seasonal cycle and a linear trend is fit.
4a ), yet is not found in the other Tier 1 methodologies. The spatial mean seasonal cycles of the CO 2 flux densities [mol m -2 yr -1 ] for each region are shown in Fig. 5  for Tier 1 methodologies: pCO 2 climatology, median of atmospheric inversions, and median of ocean biogeochemical models; additionally, we include results from the SOCAT MPR and pCO 2 database methods (all regions except the Arctic). We include BATS and ESTOC in the North Subtropics. In Table  5 , we present the correlation coefficients for the seasonal cycles in each region. In the Arctic (Fig. 5a ), all three methods have near zero fluxes for most of the year due to ice-cover, and a small drawdown in summer, which leads to good correlation of the pCO 2 climatology to the atmospheric inversions and ocean biogeochemical models (Table  5a ).
This pattern is consistent with detailed studies in the subpolar gyre  (Chierici et al. , 2009; Olsen et al. , 2008; Rödenbeck et al. , 2012; Watson et al. , 2009). However, the mean SOCAT MPR sink is much lower than that of the other methodologies (Fig. 3b ). It should be noted, however, that the SOCAT MPR only extends to 65 • N, and hence does not cover the whole of the RECCAP subpolar region. This predominantly biologically driven season cycle is also evident in the pCO 2 database approach, which is notable because, even though a significant amount of data is common between  SOCAT and Takahashi et al. (2009) , the data treatment to derive the RECCAP regions' surface pCO 2 values are quite different (Sect. 2. 2). Finally, the atmospheric inversions have a seasonal cycle that is significantly out of phase with the ocean models, with their median peaking in September, most possibly due to the significant terrestrial influence in this region bordered by large continents.