dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysing the sequences produced for this paper along with those of the other species of Lumbricidae in the previous publication  Porco et al. 2013 [40] , we were able to recover three MOTUs corresponding to P. gestroi, E. tellinii and the new species E. crodabepis sp. nov. using a 11% threshold value (data not shown). The mean intraspecific divergence found in these three species (P. gestroi 4. 79% (ranging from 0% to 8. 19%), E. tellinii 1. 25% (ranging from 0% to 4. 09%), E. crodabepis sp. nov. 4. 55% (ranging from 0% to 7. 43%), contrasted with a high interspecific mean divergence reaching 18. 64% (range 13. 86% to 21. 97%-Fig  1 ) confirming the existence of a clear barcode gap for the dataset (Table  3 ). These ranges of genetic divergence are consistent or exceed those measured among species in Lumbricidae in previous studies  [40, 41]  further confirming the separate specific status of the two taxa concerned.
Figs 3 and 4; S3 Fig. Specimens studied were collected in different localities in Veneto region (Table 1) Holotype: Crevada 3 [BOLD sampleID = IT_MGP_Crevada_3, Genbank accession = KT352951], in the Verona Natural History Museum. Paratypes: Crevada 1 (P1), Crevada 2 (P2), Crevada 4 (P3) University of Padua, HNHM 6899 (P4), HNHM 12678 (P5) deposited in the Hungarian Natural History Museum, Budapest. The name of the new species is taken from an acronym dedicated to Giovanni Canestrini, Daniele Rosa, Charles Darwin, Antonio Berlese, Pietro Omodeo and Filippo Silvestri, for various reasons relating to their scientific contributions to soil biology and earthworm studies in Italy and further abroad. The new taxon may be cited as "Eophila crodabepis Paoletti, 2016 in Paoletti et al. 2016".
Figs 3 and 4 ; 4 Figs 3 and 4; S3 Fig. Specimens studied were collected in different localities in Veneto region (Table 1) Holotype: Crevada 3 [BOLD sampleID = IT_MGP_Crevada_3, Genbank accession = KT352951], in the Verona Natural History Museum. Paratypes: Crevada 1 (P1), Crevada 2 (P2), Crevada 4 (P3) University of Padua, HNHM 6899 (P4), HNHM 12678 (P5) deposited in the Hungarian Natural History Museum, Budapest. The name of the new species is taken from an acronym dedicated to Giovanni Canestrini, Daniele Rosa, Charles Darwin, Antonio Berlese, Pietro Omodeo and Filippo Silvestri, for various reasons relating to their scientific contributions to soil biology and earthworm studies in Italy and further abroad. The new taxon may be cited as "Eophila crodabepis Paoletti, 2016 in Paoletti et al.
(2013)  used a soil-type-based rainfall-runoff model  (Lazzarotto et al. , 2006)  combined with an empirical model of soil SRP release derived from rainfall simulation experiments over soils with different P content and manure application level/timing  (Hahn et al. , 2012)  to simulate daily SRP load from critical sources areas. A second key issue, linked to the question of model complexity, concerns model calibration and evaluation. Both calibration and evaluation require assessing the fit of model outputs with observation data. However, observation data are generally not directly comparable with model outputs, because of incommensurability issues and/or because they contain errors  (Beven, 2006 (Beven, , 2009)). Typically, predicted daily concentrations and/or loads are evaluated against data from grab samples collected on a daily or weekly basis.
The water samples were filtered on-site, immediately after grab sampling and after 1-2 days in the case of autosampling. They were analysed for SRP (ISO 15681) within a fortnight. To assess uncertainty in daily SRP concentration related to sampling time, storage and measurement errors, a second grab sample was taken at a different time of the day (between 11:00 and 15:00 local time) in 36 instances during the study period. The second sample was analysed within 24 h with the same method; this second data set is referred to as verification data set, as opposed to the reference data set. Among the 36 pairs of comparable daily samples, 12 were taken during storm events and 24 during baseflow periods. To assess uncertainty in high-frequency SRP concentration during storm events due to delayed filtration of autosampler bottles, five grab samples were taken during the course of four distinct storms and were filtered immediately.
Soil P sub-model The soil P sub-model is empirically derived from soil pore water monitoring data  (Dupas et al. , 2015c) , specifically assuming that the background SRP concentration in the soil pore water of a given layer is proportional to soil Olsen P; seasonal increases in P availability compared to background conditions are determined by biogeochemical processes, controlled by antecedent temperature and soil moisture. Data show that SRP availability in the soil pore water increases following periods of dry and hot conditions  (Dupas et al. , 2015c). Hence, SRP transfer is modelled with parameters that describe both mobilization and transfer to the stream. A different parameter is used to simulate transfer via overland flow and sub-surface flow. F SRP overland = Coef SRP overland • P Olsen • q overland , (2) F SRP sub-surface = Coef SRP sub-surface • P Olsen • q sub-surface , (3) where F SRP overland and F SRP sub-surface are SRP transfer via overland flow and sub-surface flow for a given soil layer respectively, q overland and q sub-surface are water flows from the same pathways.
This resulted in some short sections of data loss. Methane data were manually screened for spikes resulting from ambient air entry at the melterhead (see also Sect. 3. 2) because an automated screening algorithm proved too aggressive, resulting in the removal of real variability, as confirmed by discrete CH 4 measurements. Methane and chemistry data were mapped onto a depth scale using high-resolution (0. 1-0. 5 Hz acquisition rate) liquid conductivity data and time-depth relationships recorded by system operators. A constant melt rate for each metre length of core is assumed. Depth scale uncertainties are estimated to be ±2 cm (2σ ). The ice and gas age scales used for each ice core are listed in Table  1. For comparison, discrete samples from the Tunu13 ice core were analysed at Oregon State University for methane concentration and total air content.
Firn air transport models We compare our empirical data to theoretical model predictions of CH 4 concentrations in closed bubbles resulting from layered gas trapping produced by the Center for Ice and Climate (CIC), Copenhagen, firn air transport model  (   (Mitchell et al. , 2013)  and NEEM-2011-S1 continuous CH 4 data  (Rhodes et al. , 2013 (Rhodes et al. , ). al. , 2012)). This model includes parameterisation of stochastic gas trapping related to local density variability  (Mitchell et al. , 2015). All experiments are run for the WAIS Divide ice core site because high-resolution local density data are available, as well as firn air sample data needed to calibrate the diffusivity profile in the open pores. Model simulations are performed at 1 cm vertical resolution to accurately capture the influence of layered bubble trapping.
Evidence for layered bubble trapping Our results demonstrate that the quasi-annual variability previously observed in the ice phase of the NEEM-2011-S1 core  (Rhodes et al. , 2013)  is not unique to NEEM or to Greenlandic ice. The question now is: what causes it? If it is an artifact of layered bubble trapping, as speculated for NEEM-2011-S1, the observed decimetre-scale variability should respond in a predictable way to several factors that vary over time and between ice core sites. We therefore systematically examine our empirical data to assess the influence of each factor and judge whether any relationship is consistent with the mechanism of layered bubble trapping.
1 ). We therefore define high-frequency non-atmospheric CH 4 variability in excess of analytical noise as "trapping signal". This analysis was repeated on the high-frequency CH 4 residual records from other ice cores: B40, NEEM-2011-S1, D4 and NGRIP (Fig. 5a-d ). For NGRIP, only data from Tunu13 (e). σ -CH 4 is calculated every 10 years for intervals of 40 years duration (except for 5 NGRIP data points (cross symbols), which are discrete 10-year intervals with no overlap, due to poor core quality and discontinuous record). Linear regression of growth rate and σ -CH 4 is displayed where appropriate. A linear fit is applied to Tunu13 and D4 data with growth rates > ±0. 4 ppb yr -1 and to NGRIP data with growth rates > ±0. 1 ppb yr -1. Panel (f) displays data from Tunu13, D4 and NGRIP with firn air transport model output for the WAIS Divide ice core (red).
1050-1240 and 1774-1860 AD (gas age) were used, the latter with a 10 years length window to avoid data gaps. For NEEM-2011-S1, data from 1450-1840 AD were used. Any 40-year time window with a data gap > 5 years duration was discarded from analysis. We note that the CH 4 growth rate recorded in the ice core is not strictly equivalent to the atmospheric growth rate because firn-based smoothing may have caused some damping of the signal (Fig. 2 ). The B40 record is significantly affected by firn-based smoothing (Fig. S2 ), which reduces the growth rate captured by the ice core archive. The B40 record is also severely impacted by system- based smoothing (Fig. S2 ), which damps the trapping signal to within range of the analytical noise for much of the record, except the section displayed in figure  4.
, 2011). It is difficult to test the effects of density layering because we do not have the highresolution density information required to do so. However, we can use the CIC firn air transport model, which utilises high-resolution density data for the WAIS Divide ice core, to make a prediction. In these simulations, we define the density layering to be ρ layer = ρ-< ρ> with the local firn densities (ρ) as given by the high-resolution measurements, and the bulk density (< ρ >) as given by a spline fit to those data. We then run the model several times with a density profile that equals ρ = < ρ> +αρ layer. By varying the scaling parameter α between 0 and 1. 6 we can effectively control the magnitude of the firn density layering. As we would expect, no highfrequency CH 4 trapping signal is produced in the absence of density layering (α = 0; Fig.
Acknowledgements. This work was supported by  US National Science Foundation (NSF)  grants  1204172 ,  0944552 ,  1204176  and  0909541  and  NSF Partnerships in International Research and Education (PIRE)  Grant  0968391. This work was additionally supported by the  French ANR  program  RPD COCLICO  ( ANR-10-RPDOC-002-01 ) and received funding from the  European Research Council  under the European Community's  Seventh Framework Program   FP7/2007-2013  Grant Agreement # 291062  (project  ICE&LASERS ). Jeffrey Severinghaus  and  David Etheridge  provided insightful reviews that improved this manuscript. We thank  Nathan Chellman ,  Daniel Pasteris ,  Larry Layman  and  Amber Zandanel  for laboratory assistance,  Nicole Rocco  for Summit melt layer measurements, and  Julia Rosen  for use of OSU firn model.
Evidence on the link between implicit attitudes and behaviour is mostly based on research conducted in experimental settings rather than on real-life data. An innovation of the present research is the measurement of the impact of both implicit and explicit attitudes to the environment on actual consumer behaviour. The focus of the article is on food choices, which account for a large component of the carbon footprint of UK households (e. g. Panzone et al. , 2013). The next section reviews the links between socio-demographics, attitudes, and consumer behaviour with relation to sustainable consumption. Section 3 describes the dataset used in the empirical analysis, which contains demographic characteristics, explicit and implicit attitudes, and actual expenditures for a sample of consumers at Tesco, the largest UK food retailer.
Finally, a dummy variable identified whether the respondent indicates Tesco as the main store for the household's grocery shopping. Summary statistics on demographics are reported in Table  3 , while attitudes are reported in Table  4. Of the 916 respondents who completed the online IAT, 21 (2. 29%) were removed from the dataset due to excessively fast responses (<300ms) (as indicated in  Greenwald et al. , 2003; and Lane et al. , 2007) , reducing the sample to 895 participants. For the remaining respondents, the IAT score was calculated according to the improved scoring algorithm developed by  Greenwald et al. (2003) , where a positive score reflects an implicit preference for sustainability whereas a negative score indicates a preference for unsustainability. The resulting IAT scores ranges from -0.
The use of an objective measure of behaviour determines whether attitudes match true rather than imagined environmental performance. For comparison, table 8 reports the same mediation analysis using total F&D sales (in logarithmic form) in 2013, as previous research suggests that environmentallyfriendly consumption requires the payment of a price premium (see e. g. Lusk et al. , 2007) , enabling aggregated sales to serve as a proxy measure of sustainable consumption. The dataset contains total sales purchased in the 12 months leading up to the survey. This variable allows for the possibility that environmental attitudes increase expenditures on food to pay for a higher environmental quality, with or without reducing quantity consumed  (Kotchen and Moore, 2008). Table  7a  reports the restricted equations that follow Baron and Kenny (1986), using standardised coefficients.
Limitations of this study While the present study presents a novel approach to the study of the economic psychology of sustainable food consumption using actual supermarket expenditures jointly with intrinsic and extrinsic attitudes, it has some limitations. Firstly, the analysis focuses on a small subset of products that make the basket sustainable. Secondly, consumption is only measured in terms of consumer expenditure in only one retailer. The analysis tries to account for these problems by adjusting for whether consumers primarily shop in Tesco, and disaggregating sales in subcategories to reach progressively smaller units of analysis. As a result, whilst the data refers to the largest UK retailer, results might not be fully generalizable to the UK population. This exercise contributes to the literature on sustainable consumption by observing the role of attitudes and demographics outside the laboratory, and at different stages of the demand formation process.
Acknowledgements 'The research presented in this article was funded by the  Sustainable Consumption Institute, University of Manchester. We would like to acknowledge the excellent support of  Zakaria Haeri  and  Chris Gartside  (dunnhumby) and  Craig Bright  (eDigitalResearch) in the preparation of the data used in this paper. We would also like to express our gratitude to  Yu Li  for his invaluable assistance in programming the IAT. We are also grateful to  Viviana Albani ,  Elisa Bellotti ,  David Evans ,  Dan Rigby ,  Dale Southerton ,  Alan Warde ,  Ada Wossink , and  Luke Yates  for useful comments and conversations. Finally, we take this opportunity to thank anonymous members of the  Tesco Shopper Thoughts Panel  for their time and willingness to take part in the current study.
This article used secondary data from scientific publications and technical reports, i. e. - Information from the scientific literature was preferred to data from technical reports. When data were different for the same variable, the smallest estimate was used in order to limit the risk of an overstatement of income or cost. A simplified cost-benefit analysis was used to evaluate the financial benefits of trophy hunting in Cameroon. By definition, the financial benefits relate to the revenue streams to the different actors  (Brent 2006). Inflows are made from sales of safari and of the products associated with this activity. They are gross financial benefits, or turnover. outflows are the actual costs incurred by the actors to obtain financial benefits. The total financial cost aggregates investment, operating and transaction costs.
The difference between the gross financial benefit and the financial cost is the net benefit, i. e. the profit that the actors derive from the activity. In contrast with a standard cost-benefit analysis, this assessment covered one year only, without planning the evolution of the sport hunting sector over the medium and long-term. The objective was to evaluate its financial and economic importance on a national scale for one year in order to compare this assessment to macroeconomic aggregates. To do this, the added value of the sector was calculated and Carpenter (1999) and  Roulet (2004) , once updated to 2012. These average prices of safari package are combined to the quantitative data collected for our sample of 10 hunting areas to assess the average turnover of trophy hunting per hectare.
ACKNoWLEDGEMENTS This research was conducted for the study "Etude de l'importance économique et sociale du secteur forestier et faunique du Cameroun" with the financial support of the Cameroon forest and Environment Sector Programme. The authors thank Zakari Walde, Mathias Ngassi, and the Direction of Wildlife and Protected Areas of the Ministry of forests of Cameroon which provided a great deal of data, as well as Peter Biggins and four referees for their inputs on a preliminary version of this article.
However, its operational use is easy, computationally inexpensive and low data  demanding. The initialization, before a forecasting use, consists in calculating soil moisture conditions using antecedent precipitation observations. Precipitation used for calibration and initialization came from a 6-hour dataset covering the 1992-2014 period. Calculated and furnished by Meteo-France, this dataset represents total precipitation (liquid and solid) spatially averaged over each basin. These precipitation data were also used as input to calculate simulated streamflow against which the evaluation of streamflow forecasts were made (see section 4 and 5). Streamflow observations used for calibration and initialization came from CNR's gauging stations at the outlet of each basin. The dataset is available at an hourly time step, covering a 18 to 26 year period depending on basins.
Ensemble forecasts from EPS We used ensemble forecasts from two different NWP models: the Ensemble Prediction System of the Integrated Forecast System (IFS-EPS) from the European Centre for Medium-range Weather Forecasts (ECMWF) and the Global Ensemble Prediction System (GEFS) from the National Centers for Environmental Prediction (NCEP). More information about ensemble forecasting can be found in  [24]  and  [25]. The variable of interest in this study is the 6-hour accumulated total precipitation, with a lead time from 6 to 120 hours. Forecasts are taken from the 00 UTC cycle. Characteristics of these forecasts are given in Table  2. Both datasets have been extracted from the TIGGE archive  [25]  over the 2007-2014 period. As raw forecasts are grid-based, Thiessen-based spatial averaging has been made in order to fulfil the requirement of the lumped rainfall-runoff model (a single value par basin).
When two predictors are specified for a single level, the analogy criterion is computed for both and the mean is considered. The most the candidate situations found in the reanalyses archive are analogue to the target ones, the most the precipitation forecasts issued from these situations are expected to be reliable. Analogue methods therefore require a long common archive of reanalyses and precipitation observations. This is especially true for extreme precipitation forecasting, where driven atmospheric conditions leading to severe events are supposed to be rare. If only "mediocre" analogues (not so similar) are found in the archive, precipitation might be under-forecasted. The CNR's 6-hour precipitation observation dataset (cf. 3. 1. ), which covers a 23 year period, is here the limiting factor.
Another requirement is the consistency of target and candidate situations. At least, grid resolutions must match in order to calculate analogy criteria. Better, the NWP models must be the same for both forecasts and reanalyses, in order to find best analogies possible. However, the constant evolution of NWP models makes this constraint too restricting. In this study, we relaxed it considering the NWP model for forecasting and for reanalyses coming from the same meteorological centre. Differences in truncations, parametrization may exist, but the model core is assumed to be similar. The two same meteorological centres than for ensemble forecasts were used: ECMWF and NCEP. Two different analogue-based forecast datasets were therefore produced. Thereafter, they will be referred as ECMWF-Ana and NCEP-Ana.
As shown in Table  4 , the respective reanalyse datasets used were ERA-Interim  [31]  and CFSR  [32]. The respective deterministic forecasts used were the control forecasts of the aforementioned IFS-EPS and GEFS models. Reanalyses and control forecasts were all interpolated at the 0. 5° spatial resolution. Spatial domains for levels 1 and 2 were optimized separately for each reanalyse dataset, in perfect-prognosis conditions (both target and candidate situations come from reanalyses).
A reordering of analogue-based forecasts is therefore necessary, the term reordering standing for the association of forecasted values for different lead times. If no temporal correlation (or autocorrelation) would be observed, a random reordering could be sufficient. However, an autocorrelation pattern does exist on 6-hour precipitation. Spearman's rank correlation coefficient (Rho) for autocorrelation has been calculated on the observed precipitation observation dataset, after having taken away zero precipitation values. Rho coefficients for lag 1 (i. e. between two 6-hour time steps) were found between 0. 3 and 0. 5 depending on basins, and negative for higher lags. It means that positive precipitation values are correlated with the preceding and the following ones. With a random reordering of the forecasts, the probability for a high value (in the discrete distribution) at time step t to be associated with another high value at time step t+1 is low.
This is nevertheless highly probable in reality, where successive high precipitation time steps may cause flood events. As a consequence, the reordering has to respect the temporal correlation observed in precipitation observations. To address this issue,  [33]  proposed the Schaake shuffle reordering method, whose successive steps are briefly described below. -For a forecast of M members to reorder, M precipitation time-series lasting 120 hours each are randomly selected among the observation dataset. -For each 6-hour time step, these M observations are sorted and their rank noted. For example, one randomly selected time serie might be ranked 29 th for time step 1, 34 th for time step 2, etc. Equal values are treated randomly. -Forecast values are likewise sorted, and the rank structure obtained above from observations is used to link forecast values: the forecast value being ranked 29 th for time step 1 will be linked with the one being ranked 34 th for time step 2, etc.