dataesr/scientific-paragraphs-categorization huggingface dataset:
Fossil-fuel and cement emissions and their uncertainty The calculation of global and national CO 2 emissions from fossil-fuel combustion, including gas flaring and cement production (E FF ), relies primarily on energy consumption data, specifically data on hydrocarbon fuels, collated and archived by several organisations  (Andres et al. , 2012). These include the Carbon Dioxide Information Analysis Center (CDIAC), the International Energy Agency (IEA), the United Nations (UN), the United States Department of Energy (DoE) Energy Information Administration (EIA), and more recently also the PBL Netherlands Environmental Assessment Agency. We use the emissions estimated by the CDIAC  (Boden et al. , 2013). The CDIAC emission estimates are the only ones that extend back in time to 1751 with consistent and welldocumented emissions from fossil-fuel combustion, cement production, and gas flaring for all countries, including their uncertainty  (Andres et al.
emissions except to the extent that they are captured in the UN energy data and counted as gas "flared or lost". The published CDIAC data set has 250 countries and regions included. This expanded list includes countries that no longer exist, such as the USSR or East Pakistan. For the budget, we reduce the list to 219 countries by reallocating emissions to the currently defined territories. This involved both aggregation and disaggregation, and does not change global emissions. Examples of aggregation include merging East and West Germany to the currently defined Germany. Examples of disaggregation include reallocating the emissions from the former USSR to the resulting independent countries. For disaggregation, we use the emission shares when the current territory first appeared. For the 2 most recent years, 2011 and 2012, the BP statistics are more aggregated, but we retain the detail in CDIAC by applying the same growth rates to individual countries in CDIAC as in the aggregated regions in the BP data set.
, 2011a). Due to the availability of the input data, detailed estimates are made for the years  1997, 2001, 2004, and 2007  (using the methodology of  Peters et al. , 2011b)  using economic and trade data from the Global Trade and Analysis Project version 8. 1 (GTAP;  Narayanan et al. , 2013). The results cover 57 sectors and 134 countries and regions. The results are extended into an annual time series from 1990 to the latest year of the fossil-fuel emissions or GDP data (2011 in this budget), using GDP data by expenditure in current USD (from the UN National Accounts main Aggregrates database; UN, 2013c) and time series of trade data from GTAP (based on the methodology in  Peters et al. , 2011b). The consumption-based emission inventories in this carbon budget incorporate several improvements over previous versions (Le  Quéré et al.
, 2013; Peters et al. , 2012b Peters et al. , , 2011b)). The detailed estimates for 2004 and 2007 and time series approximation from 1990-2011 are based on an updated version of the GTAP database  (Narayanan et al. , 2013). We estimate the sector level CO 2 emissions using our own calcula-tions based on the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match the CDIAC estimates from the most recent carbon budget. We do not include international transportation in our estimates of national totals, but include them in the global total. The time series of trade data provided by GTAP covers the period 1995-2009 and our methodology uses the trade shares of this data set. We do not provide an uncertainty estimate for these emissions, but based on model comparisons and sensitivity analysis, they are unlikely to be larger than for the territorial emission estimates  (Peters et al.
Some of these activities lead to emissions of CO 2 to the atmosphere, while others lead to CO 2 sinks. E LUC is the net sum of all anthropogenic activities considered. Our annual estimate for 1959-2010 is from a bookkeeping method (Sect. 2. 2. 1) primarily based on net forest area change and biomass data from the Forest Resource Assessment (FRA) of the Food and Agriculture Organization (FAO) which is only available at intervals of 5 years  (Houghton et al. , 2012). Interannual variability in emissions due to deforestation and degradation have been coarsely estimated from satellite-based fire activity in tropical forest areas (Sect. 2. 2. 2; Giglio et al. , 2010; van der Werf et al. , 2010). The bookkeeping method is used mainly to quantify the E LUC over the time period of the available data, and the satellite-based deforestation fire information to reveal interannual variability due to tropical deforestation fires.
The data are based on countries' self-reporting, some of which include satellite data in more recent assessments (Table 4). Changes in land cover other than forest are based on annual, national changes in cropland and pasture areas reported by the FAO Statistics Division  (FAOSTAT, 2010). LUC country data are aggregated by regions. The carbon stocks on land (biomass and soils), and their response functions subsequent to LUC, are based on FAO data averages per land cover type, per biome and per region. Similar results were obtained using forest biomass carbon density based on satellite data  (Baccini et al. , 2012). The bookkeeping model does not include land ecosystems' transient response to changes in climate, atmospheric CO 2 and other environmental factors, but the growth/decay curves are based on contemporary data that will implicitly reflect the effects of CO 2 and climate at that time.
g. there is more burning and clearing of forests in dry years) that are not represented by other methods. The "deforestation fire emissions" assume an important role of fire in removing biomass in the deforestation process, and thus can be used to infer gross instantaneous CO 2 emissions from deforestation using satellite-derived data on fire activity in regions with active deforestation. The method requires information on the fraction of total area burned associated with deforestation versus other types of fires, and can be merged with information on biomass stocks and the fraction of the biomass lost in a deforestation fire to estimate CO 2 emissions. The satellite-based fire emissions are limited to the tropics, where fires result mainly from human activities. Tropical deforestation is the largest and most variable single contributor to E LUC.
, 2011) , which provided annual, half-degree, fractional data on cropland, pasture, primary vegetation and secondary vegetation, as well as all underlying transitions between land-use states, including wood harvest and shifting cultivation. This data set used the HYDE  (Klein Goldewijk et al. , 2011)  spatially gridded maps of cropland, pasture, and ice/water fractions of each grid cell as an input. The HYDE data are based on annual FAO statistics of change in agricultural area  (FAOSTAT, 2010). For the year 2012, the HYDE data set was extrapolated from 2011, based on the trend in agricultural area over the previous 5 years. The HYDE data set is independent from the data set used in the bookkeeping method  (Houghton, 2003 and updates) , which is based primarily on forest area change statistics  (FAO, 2010).
Although the Hurtt land-use change data set indicates whether landuse changes occur on forested or non-forested land, typically only the the changes in agricultural areas are used by the models and are implemented differently within each model (e. g. an increased cropland fraction in a grid cell can either be at the expense of grassland, or forest, the latter resulting in deforestation; land cover fractions of the non-agricultural land differ between models). Similarly, model-specific assumptions are also applied for the conversion of wood harvest mass or area and other product pools into carbon in some models (Table  5 ). The DGVM model runs were forced by observed monthly temperature, precipitation, and cloud cover fields, provided on a 0. 5 • × 0. 5 • grid and updated to 2012 by the Climatic Research Unit  (Harris et al.
Uncertainty assessment for E LUC Differences between the bookkeeping, the addition of firebased interannual variability to the bookkeeping, and DGVM methods originate from three main sources: the land cover change data set, different approaches in models, and in the different processes represented (Table  5 ). We examine the results from the nine DGVM models and of the bookkeeping method to assess the uncertainty in E LUC. The uncertainties in the annual E LUC estimates are examined using the standard deviation across models, which ranged from 0. 3 to 0. 9 GtC yr -1 , with an average of 0. 5 GtC yr -1 from 1959 to 2012 (Table  7 ). The mean of the multi-model E LUC estimates is higher than the bookkeeping estimate used in the budget with a mean absolute difference of 0. 3 GtC for 1959 to 2010.
The correlations between decades come from (1) common biases in system boundaries (e. g. not counting forest degradation in some models); (2) common definition for the calculation of E LUC from the difference of simulations with and without LUC (a source of bias vs. the unknown truth); (3) common and uncertain land-cover change input data which also cause a bias, though if a different input data set is used each decade, decadal fluxes from DGVMs may be partly decorrelated; and (4) model structural errors (e. g. systematic errors in biomass stocks). In addition, errors arising from uncertain DGVM parameter values would be random but they are not accounted for in this study, since no DGVM provided an ensemble of runs with perturbed parameters. Prior to 1959, the uncertainty in E LUC is taken as ±33 %, which is the ratio of uncertainty to mean from the 1960s (Table 7), the first decade available.
The annual growth rate is estimated from atmospheric CO 2 concentration by taking the average of the most recent December-January months corrected for the average seasonal cycle and subtracting this same average one year earlier. The growth rate in units of ppm yr -1 is converted to fluxes by multiplying by a factor of 2. 120 GtC per ppm  (Prather et al. , 2012)  for comparison with the other components. The uncertainty around the annual growth rate based on the multiple stations data set ranges between 0. 11 and 0. 72 GtC yr -1 , with a mean of 0. 60 GtC yr -1 for 1959-1980 and 0. 19 GtC yr -1 for 1980-2012, when a larger set of stations were available. It is based on the number of available stations, and thus takes into account both the measurement errors and data gaps at each station. This uncertainty is larger than the uncertainty of ±0.
(2014, version "oc_v1. 2") using an inversion method based on observed oceanic partial pressure of CO 2 (pCO 2 ) derived from the Surface Ocean CO 2 Atlas (SOCAT v2), a database of qualitycontrolled surface ocean fugacity of CO 2 (pCO 2 corrected for the non-ideal behaviour of the gas;  Bakker et al. , 2014; Pfeil et al. , 2013). Interannual variability in ocean CO 2 was also estimated with an update of  Park et al. (2010)  based on regional correlations between surface temperature and pCO 2 , changes in surface temperature observed by satellite, and wind speed estimates also from satellite data for 1990-2009  (Atlas et al. , 2011). This estimate provides a data-based assessment of the interannual variability combined with a model-based assessment of the trend and mean in S OCEAN. We use the data-based product of Khatiwala et al.
Data access The data presented here is made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights of how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data are given at the top of each page in the accompanying database, and summarised in Table  2. The accompanying database includes an Excel file organised in the following spreadsheets (accessible with the free viewer http://www.
Acknowledgements. We thank all people and institutions that provided data used in this carbon budget,  P. Cadule ,  C. Enright ,  J. Ghattas , and  G. Hurtt  for support to the model simulations,  F. Joos  and  S. Khatiwala  for providing historical data,  M. Heimann  and  G. McKinley  for their comments and suggestions. The observations and modelling analysis were possible thanks to the generous support from the author's institutions and funding from multiple agencies around the world. We thank the many researchers and funding agencies responsible for the collection and quality control of the data included in SOCAT, and the support of the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS) , and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER).
The  UK Natural Environment Research Council (NERC)  provided funding to  C. Le Quéré ,  R. Moriarty  and the GCP though their International Opportunities Fund specifically to support this publication (project  NE/103002X/1 ). R. J. Andres  and  T. A. Boden  were supported by the  US Department of Energy, Office of Science, Biological and Environmental Research (BER)  programs under  US Department of Energy  contract  DE-AC05-00OR22725. G. P. Peters  and  R. M. Andrews  were supported by the  Norwegian Research Council  ( 221355 ). A. Arneth ,  A. Omar ,  C. Le Quéré ,  J. Schwinger ,  P. Ciais ,  P. Friedlingstein ,  P. Regnier ,  J. Segschneider ,  S. Sitch  and  S. Zaehle  were supported by the  EU   FP7  for funding through projects  GEOCarbon  ( 283080 ),  COMBINE  ( 226520 ),  CARBOCHANGE  ( 264879 ),  EMBRACE  ( GA282672 ), and  LUC4C  ( GA603542 ).
In particular, observations with the LAD will enable deep spectroscopic and time variability studies across objects exhibiting a wide range of highly energetic physical phenomena powered by accretion, thermonuclear and magnetic processes as well as various types of gravitational collapse. For objects ≥1-2mCrab it will provide the best variability information and mid-resolution spectroscopy observations ever across the 2-30 keV band. Given the incredible leap in effective area, FoV, and sensitivity of the instruments on-board LOFT, the range of astrophysical problems that will receive new stimuli from the mission's capabilities is enormous and cannot be summarized here. A more detailed discussion can be found in the mission Yellow Book (to be published by ESA) and in the large number of scientific papers mentioning the breakthrough capabilities of LOFT that can be found through the Astrophysics Data System (ADS).
Monitoring the X-ray sky We emphasised above that many of the most interesting sources that LOFT will observe are transients and/or undergo changes in their spectral/timing states that need to be monitored carefully in order to schedule observations at the right moment. For this reason, the ground segment and science operation strategy of LOFT have been designed in order to maximize the science return of the mission and the exploitation of the LOFT data products by the science community at large. Both the data from the LAD and the WFM will be inspected as soon as available on the ground in order to search for transient sources and/or interesting events. A preliminary version (NRT, near real time) of the WFM data will be typically available within ~3 hours from the observation, while the LAD NRT data might require up to 1 day to be fully accessible on the ground.
The Grell 3D cumulus scheme was used, with shallow cumulus option on for runs with the MYNN ( Mellor-Yamada-Nakanishi-Niino) PBL scheme and off for runs with the TEMF (total energy-mass flux) PBL scheme. The model was initialized at 00:00 UTC each No observed data were directly assimilated into WRF, nor were the WRF runs nudged toward any analysis. Most of these configuration choices were the same as used for California in  Angevine et al. (2012). References for all WRF options can be found in  Skamarock et al. (2008). We used a version of the FLEXPART Lagrangian particle dispersion model  (Stohl et al. , 2005)  modified to use WRF output  (Brioude et al. , 2013a). FLEXPART-WRF uses the same grid spacing as in WRF. FLEXPART-WRF solves turbulent motion in a Lagrangian framework using firstorder Langevin equations.
We defined the FLEXPART-WRF output grid (which is independent of the transport calculation) with a 12 km grid spacing in both horizontal dimensions and 28 vertical layers, each 100 m thick. The horizontal grid corresponds to that used for the driving WRF simulations. Particles are grouped into six age classes on output, with maximum ages of  3, 6, 12, 24, 48 , and 120 h since emission. Approximately 1. 8 million particles were emitted each day of the simulation. No chemical transformation or deposition was simulated. The spatial and temporal pattern of emissions is that of carbon monoxide (CO) specified according the US EPA 2011 National Emission Inventory, version 1, available as of 8 November 2013 (http://www. epa. gov/ttn/chief/ net/2011inventory. html#inventorydoc). Gridded (4 km resolution), hourly emissions for a July average weekday in 2011 have been derived from this inventory, and are publically available at the WRF-Chem data site: ftp://aftp.
fsl. noaa. gov/ divisions/taq/emissions_data_2011/. Specific details on the files and data sets used for spatial and temporal partitioning are supplied in the readme. txt file at the data site. Because the map projection and domain used in the WRF and FLEXPART-WRF simulations are chosen to overlap with the US EPA emissions grid, hourly emissions from the 4 km National Emissions Inventory (NEI) inventory are simply combined together within the 12 km grid resolution used here. Details of the emissions are not directly relevant here, since all runs use the same emissions and results are normalized. When comparing with observed CO, it must be kept in mind that there are a number of CO sources not accounted for in these simulations. These include biomass burning, class-3 commercial marine vessels, and oxidation of methane and volatile organic compounds.
Code and data availability WRF is available from http://wrf-model. org. FLEXPART-WRF is available from http://flexpart. eu and from the online supplement to Brioude et al. (2013a). Interested parties may contact us to access the (large) amount of WRF and FLEXPART-WRF output used in this study. The Supplement related to this article is available online at doi:10. 5194/gmd-7-2817-2014-supplement.
Despite this, interactions between superpredators and mesopredators are often ignored. In spite of many studies suggesting that predation is selective on certain classes of individuals, the role of selective predation in moderating the overall impact of predators remains poorly known. Research linking selective predation to its subsequent effects on populations is needed to help determine whether such biases are responsible for variation in the impact of predators on population dynamics. Here, we take an empirical approach combined with a simulation exercise to determine whether predation of other predators (superpredation) is selective and examine the role of selective predation in moderating the overall impact of predators on prey populations. To do this, we take advantage of long-term longitudinal data collected on individuals of an established population of mesopredators, spanning the colonization and increase in abundance of a superpredator.
Acknowledgements Thanks to  J. Reid ,  S. Redpath ,  A. Beckerman  and an anonymous reviewer for their helpful comments on a previous version of the manuscript. This work was partly funded by a  Natural Environment Research Council  studentship  NE/J500148/1  to SH and a grant  NE/F021402/1  to XL and by  Natural Research Limited. Forest Research funded all the fieldwork on goshawks, tawny owls and field voles during 1973-1996. We thank  B. Little ,  P. Hotchin ,  D. Anderson  and all field assistants for their help with data collection and Forest Enterprise,  T. Dearnley  and  N. Geddes  for allowing and facilitating work in Kielder Forest. In addition, we are grateful to  English Nature  and the  BTO  for kindly issuing licences annually visit goshawk nest sites.
The LDR value for the full sample is 1. 08 (with a KS p-value 0. 92), whereas the LDR value for the restricted sample is 0. 79 (with KS p-value 0. 16). Therefore, in both samples the ratio is not significantly different from unity. We therefore conclude that the unusual geometry of the ONC data should not affect the determination of either MSR or LDR. Using the data from  Andersen et al. (2011) , we first determine  We also plot the local surface density against object mass m in Fig. 5 , using the surface densities calculated for the whole non-contiguous sample. The median surface density for BDs is 0. 03≤m/M ≤0. 08 = 56 stars pc -2 , shown by the horizontal orange line, compared to 0. 08<m/M ≤1. 0 = 196 stars pc -2 for stars, shown by the horizontal black line ( LDR = 0. 29). A KS test between the two distributions gives a p-value <10 -7 that the two subsets share the same parent distribution.
We also repeated the above analysis but limited the data to objects within 1 pc of the ONC centre and found similar results, suggesting that any field star contaminants in the data do not influence our analysis. In tandem with the R SSR ratio, MSR and LDR both suggest that the spatial distribution of BDs is different to stars in the ONC. However, this may not necessarily be a primordial signature of star formation, as we have seen in N-body simulations where 6/20 clusters have a dynamical evolution that leads to spatial differences between stars and BDs.
When applied to data from the ONC, the R SSR ratio and LDR ratio -and tentative evidence from MSR -do suggest that the BDs are more spread out than stars. However, this data set is spatially incomplete, and a more comprehensive survey of the ONC would be highly desirable. Randomly distributing masses drawn from an IMF can result (in 1/20, or 5 per cent, of simulations) in a radially decreasing R ss ratio before dynamical evolution, which may or may not be mirrored in the MSR and LDR measurements. Furthermore, dynamical evolution leads to significant differences between the spatial distributions of stars and BDs in more than 25 per cent of our simulations. This implies that a large observational sample of regions/clusters is needed to assess whether the primordial spatial distributions of stars and BDs are different (which would suggest that their formation mechanisms are different).
MSR as a function of the N MST least massive objects in the observational sample as shown in Fig. 4. The data show a marginally more spread-out spatial distribution of the BDs compared to the cluster average, although the most extreme value is MSR = 0. 84 +0. 09 -0. 10 for the 36 least massive objects, which is barely significant. MSR = 1 (i. e. no mass segregation) is shown by the dashed line. MNRAS 441, 784-789 (2014) Downloaded from https://academic. oup. com/mnras/article/441/1/784/983264 by guest on 24 March 2022.
Background. This article provides a summary of the 2011 ERA-EDTA Registry Annual Report (available at www. era-edta-reg. org). Methods. Data on renal replacement therapy (RRT) for end-stage renal disease (ESRD) from national and regional renal registries in 30 countries in Europe and bordering the Mediterranean Sea were used. From 27 registries, individual patient data were received, whereas 17 registries contributed data in aggregated form. We present the incidence and prevalence of RRT, and renal transplant rates in 2011. In addition, survival probabilities and expected remaining lifetimes were calculated for those registries providing individual patient data. Results. The overall unadjusted incidence rate of RRT in 2011 among all registries reporting to the ERA-EDTA Registry was 117 per million population ( pmp) (n = 71.