dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement of dam impacts Through the community network's advocacy efforts, the Vietnamese and Cambodian governments eventually acknowledged that the Yali-Falls Dam has caused negative impacts downstream. According to  Trandem (2008: 111-12) , the first recognition came in the form of a verbal apology at a November 2002 conference, when a representative of Vietnam's Ministry of Industry stated 'we are very sorry for the losses of the people living downstream on the Se San River, caused, of course, by releasing water from Yali Falls dam's reservoir in February 2000'  (SPN 2003). The second acknowledgement came in August 2003, when, at the recommendation of SPN, the CNMC sent a facsimile to VNMC, stating the concerns of the Se San villagers and requesting that Vietnam respond accordingly.
Acknowledgements The book is first and foremost a product of its authors, who conducted fieldwork in the context of various research projects and assignments and made an additional effort to contribute the respective chapters. The  Fritz Thyssen Foundation  generously granted funding for the writeshop and some editorial work. The  Center for Development Research (University of Bonn ) graciously organized and hosted the writeshop. Special thanks also go to  Adam Fforde  and  Jörg Wischermann , who participated in the writeshop and provided valuable feedback. Finally, our regards to  Michaela Doutch , an intern at ZEF, and  Sebastian Eckert ,  ZEF  's public relations assistant, who both were a source of great support throughout the whole project and helped to bring the final manuscript into a proper shape.
Acknowledgments The authors wish to thank the two anonymous reviewers,  Ólafur Rögnvaldsson  for useful comments to the manuscript and  Anak Bhandari  for his technical support during the field campaign. In addition  Ingebjørg Aarvik ,  Marco Häberle ,  Iris Hestnes  and  Trine Jonassen  assisted in the preparation of the campaign. The  Norwegian Research Council , through the  NOTUR  project, has made super-computing resources on a Cray XE6m-200 computer at Parallab at the  University of Bergen available. The preparation of the manuscript was financially supported by the  Geophysical Institute  and the  Faculty of Mathematics and Natural Sciences at the University of Bergen  under the 'smådriftsmidler' scheme. Kolstad's work and part of Pontoppidan's work was funded through the  Research Council of Norways HordaKlim  and  R3  projects (grant numbers  245403  and  255397 ).
Termination efficiency measurements We used the RIIIG (pFAB763) measurement device to observe and estimate TE values from fluorescence measurements. We screened for and established a reference control sequence that resulted in highest expression of the downstream gfp yet did not itself initiate transcription [Equation (2), Supplementary Figure  S3 ]. The activity of the reference construct was observed in parallel with every assay. Cell cultures were grown to mid-exponential phase in deep 96-well plates in rich medium supplemented by kanamycin (Supplementary Material). Single-cell green and red fluorescence intensities were measured using an automated Guava Õ EasyCyte flow cytometer (EMD Millipore, Hayward, CA, USA). Raw data were filtered using an automated gating strategy  (29)  to ensure consistent distributions of TR CELL ratio [Equation (1), Supplementary Figure  S4 ].
This curve corresponds well to the computational results presented in the paper of Fedosov et al. [4] , who report a maximal axial diameter of 16 mm and a minimal transversal diameter between 4 and 5 mm at a force of 200 pN, as well as experimental data by Suresh et al. [12]. RBC relaxation. In order to validate the dissipation constants of the cortex itself (see equation 25), a relaxation simulation was performed. In this in-silico experiment, the cell is first stretched with a fixed force until a constant axial diameter D A of approximately 8. 9 mm is observed. Subsequently, the force is released and the change in D A over time is monitored. For a liquid viscosity of blood plasma, we found that the cortex damping coefficient c should be chosen in the order of 50 mmPas to match experimentally observed RBC relaxation dynamics (Figure  3 ).
BS acknowledges the  Agency for Innovation by Science and Technology in Flanders (IWT)  for financial support (www. iwt. be). The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant Agreement Nu 308223. (erc. europa. eu). PVL has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant agreement Nu 267038 and  Cosmetics Europe. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Atmospheric CO 2 observations Atmospheric surface CO 2 observations are obtained from a global network of more than 100 sites where CO 2 is measured either continuously (∼ 50 sites), or via discrete flask air samples (weekly; ∼ 100 sites). The current network is part of an international effort that started 55 yr ago with a continuously growing number of sites. Most of the sites are currently incorporated into the GLOBALVIEW data product (GLOBALVIEW-CO2: Cooperative Atmospheric Data Integration Project -Carbon Dioxide. NOAA ESRL, Boulder, Colorado (available at http://www. esrl. noaa. gov/gmd/ccgg/ globalview/)) and are also available through the World Data Center for Greenhouse Gases (WDCGG). However, even with current networks, the interiors of most continents, as well as key areas of the ocean (i.
CO 2 observing networks Previous work  (Law et al. , 2003; Patra et al. , 2006; Gurney et al. , 2008)  has shown that the fluxes estimated in the inverse approach can be very sensitive to the composition of the CO 2 observing network used (Fig. 1 ). This is particularly true for parts of the world where there are few observing sites; it is directly reflected in the estimated flux uncertainties (see Table  2 ) with larger values. Interannual variations (IAV) in the inversely estimated fluxes can also be sensitive to particular observing sites and the overall network composition. Accurate quantification of the flux variability may be confounded by changes in the availability of observations through the estimation time period  (Rödenbeck et al. , 2003). Most inversions attempt to minimize this spurious variability by only using sites that are available for the full period of the inversion (LSCEa, JENA, RIGC), or by making use of the interpolated data in the GLOBALVIEW data product (CCAM, MATCH, TrC, NICAM).
Note that the two Carbon-Tracker estimates (CT2011_oi, CTE2013) assimilate all data that were positively quality controlled by the inverse modelling team (i. e. removing outliers). The list of the observation sites and data selection criteria used in each of the participating inversion systems can be found in the Supplement. Overall, the number of sites varies by a factor of almost three, i. e. between 50 (JENA) and 146 (JMA) (Table  1 ). Some inversions directly assimilate raw data at the appropriate sampling time (weekly flasks or continuous record; MACC-II, CT2011_oi, CTE2013, JENA) while the other systems only use monthly mean values derived mainly from the GLOB-ALVIEW data product or from WDCGG (JMA). In general the GLOBALVIEW product uses data selected for clean-air conditions. Finally, CCAM and MATCH additionally assimilate measurements of the 13 C / 12 C isotopic ratio of CO 2 to further constrain the partition between land and ocean carbon fluxes  (Rayner et al.
However, even with the "fossil fuel" correction, the global annual totals show significant differences, up to 1 Pg C yr -1 , among the different inversions in particular years. The differences are not systematic and reflect differences between atmospheric transport mixing properties, especially the mixing of surface fluxes to the upper atmosphere, as well as differences between inverse approaches and observation data network. For example, both CarbonTracker systems (CT2011_oi/CTE2013) provide similar fluxes in 2001 and 2002, although lower than all other inversions. Note finally that the differences in the yearly mean may also reflect small differences in flux allocation between December and January (due to the "boxcar" average). To further investigate the general mean behavior of the participating inversions, Fig.
These differences are also seen in the prior fluxes (Supplement, Fig. S6 ) used by most inversions, which are mainly based on the CASA model  (Randerson et al. , 1997). It is worth noting that the inversions that do not use this prior (MACC-II, LSCEa, JENA) nevertheless largely agree with the other inversion estimates. Given the significant differences between the MACC-II/LSCEa prior (both based on two versions of the same land model) and the other priors (Supplement, Fig. S6 ), the similarity of the posterior fluxes indicates that the seasonality is driven primarily by the atmospheric data rather than the prior flux. A weak influence from the prior may be the reason why both LSCEa and MACC-II inversions show earlier maximum uptake in the North American and European regions. For Europe there seems to be greater inversion spread for the early part of the growing season than for the onset of senescence.
Acknowledgements. We first thank all PIs of the atmospheric stations whose data are the principal component of the atmospheric inversions and those who maintain instruments and collect samples (including those whose data is included in ObsPack). We also thank those who put significant work into providing atmospheric data compilations (including  Ken Masarie  for compiling ObsPack and, previously,  GLOBALVIEW-CO2 ) and hosting data sets (especially WDCGG). Wouter Peters  was partly paid from  NWO VIDI  Grant  864. 08. 012. RIGC inversion work is partly supported by  JSPS/MEXT KAKENHI-A  (grant number  22241008 ). I. T. van der  Laan-Luijkx  has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  283080 , project  GEOCARBON. Y. Niwa  thanks  Masaki Satoh ,  Hirofumi Tomita  and other members at  RIGC/JAMSTEC ,  AICS/RIKEN , and at  AORI/The University of Tokyo  for developing NICAM and thanks also  Toshinobu Machida  for providing Siberian aircraft data.
(i) x þ 1; y; z; (ii) x À y; x À 1; Àz þ 1; (iii) Ày þ 1; x À y À 1; z; (iv) Àx þ y þ 1; Àx; z; (v) Àx þ y þ 1; Àx þ 1; z; (vi) Ày þ 1; x À y; z; (vii) x; y; Àz þ 3 2 ; (viii) Àx þ 1; Ày; Àz þ 1; (ix) y þ 1; Àx þ y þ 1; Àz þ 1; (x) Ày; x À y; Àz þ 3 2 ; (xi) x À 1; y; z. Intensity data were collected on the Nonius KappaCCD X-ray diffactometer system of the Centre de diffractometrie de l'Universitede Rennes I (www. cdifx. univ-rennes1. fr). Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: RU2054).
1997 , Theurillat & Guisan 2001 , Schumacher & Bugmann 2006 , Gehrig-Fasel et al. 2007 , Wastl et al. 2012)  we regard the investigation of the human-caused fire ignition as substantial for a better understanding of fire danger for Austrian forests and the goods and services they provide. The objective of this study is to present a fire danger model to predict the spatial occurrence of human-caused fire ignitions in Austria. Records of human-caused forest fires for the period from 1993 to 2009 have been extracted from the AFFRI wildfire database and have been used together with potentially explanatory socio-economic variables in order to develop different logistic regression models and describe the influence of socio-economic factors on forest fire ignition. A model has been chosen, which represents the influence of the socio-economic factors on forest fire ignition probability the best.
Study area and fire database Austria is a central European country covering 83 855 km 2 and a population size of 8. 47 million people. Austria is a largely alpine country (68%) with a temperate to alpine climate. Average temperatures range from -10 °C in winter to 20 °C in summer. Austria has a total number of 2357 municipalities with areas ranging from 32. 4 km 2 for the smallest municipality to 466. 9 km 2 for the largest municipality. Some 47. 6% of Austria is covered with forests, out of which the main part is coniferous forest  (AFI 2011). Underlying data on forest fires have been obtained from the wildfire database, which has been created within the frame of the Austrian Forest Fire Research Initiative (AF-FRI) currently taking place at the University of Natural Resources and Life Sciences, Vienna.
Wildfire records have been obtained from municipalities, fire brigades, the OEBB (Austrian Railway Company) as well as the Federal Ministry of Agriculture and Forestry, Environment and Water Management (BMLFUW). The database includes information for each fire record on geographical coordinates and other characteristics (e. g. , municipality, cause of fire, time of day of start and end of fire, time of detection, ignition type, vegetation type and tree species affected) for the period 1993-2009  (Vacik et al. 2011 , Eastaugh & Vacik 2012). Given that this study analyzes the role of socio-economic factors for forest fire ignition, only 955 human-caused forest fires recorded in the database and matching the selected criteria (e. g. , full record of information available, size of the burned area) have been used for this study, totaling to 1660 fire records.
1995). Model events with binary or dichotomous variables -such as the presence or absence of fire ignition -are best described by means of logistic regression. Forward stepwise logistic regression has been chosen for this study, since the interest was on identifying the significance of a group of predicting variables affecting fire occurrence as well as to model ignition probability. Moreover, logistic regression proved to be relatively flexible, since it accepts a combination of continuous, categorical and non-normally distributed variables  (Bellgardt 1997). In relation to the human-caused forest fires recorded in our database, five different sets of 955 points randomly distributed over Austria have been generated with ArcGIS™ and were classified as "no fire events". These randomly generated sets of points have been joined with the 955 human-caused "fire events" from the forest fire dataset in order to be able to quantify the predictive ability of the socio-economic variables chosen.
Ignition and non-ignition points were coded in a binary format (0: no fire; 1: fire) representing the dependent variable fire ignition. In order to be able to test the final model, 100 fire ignition points and 100 no-fire ignition points were selected randomly from each of the five datasets for validation, resulting in five sets with 855 fire and 855 no-fire events for calculating the model. The analysis is based on the following function (eqn. 1): where P is the probability of occurrence of an event and z is the linear function of the independent variables (eqn. 2): The presence or absence of forest fire ignitions has been chosen as the dependent variable for the analysis. Previous studies have identified a number of human factors to be significant for wildfire ignition (Tab. 1 -  Cardille et al.
g. , agricultural area, intensity of agricultural operations); • Factors related to forest management (e. g. , density of forest area, forest roads, forestry operations, storm blow-down salvage harvesting); • Factors related to infrastructure and their density (e. g. , roads, railroads). Tab. 1 shows all independent socio-economic variables chosen for the study. Information on socio-economic factors in this study has been derived from the Statistik Austria census database on municipality level as well as from the Corine Landcover classification (Environmental Agency Austria 2012). Besides the above socio-economic data, additional variables have been ge-nerated in order to obtain socio-economic factors potentially relevant for forest fire ignition. The logistic regression analysis was performed using SPSS ® software, version 15.
Model construction In order to eliminate multicollinearity between the variables selected for this study, the Pearson correlation analysis has been conducted for all five datasets independently. All variables with a correlation higher than 0. 5 were not further considered for the model building procedure. Five different models have been calculated with the re-maining variables using the different datasets in order to choose the best fit to Austrian conditions. Various methods were tested examining the level of significance through which the variables were introduced and removed from the equation. Variables were introduced into the model according to a significance of p < 0. 05 (Wald significance) and removed from the model with a p > 0. 1 (Wald significance -see  Martinez et al. 2009 ). The Nagelkerke's R 2 test and the likelihood ratio -2LL value was used to estimate goodness-of -fit  (Menard 2008 ).
Even though other levels and approaches for assessing probability could be used (see  Castedo-Dorado et al. 2011 ) the cut-off point 0. 5 -the midpoint of the logistic function -was chosen for this study, since it is the one most extensively used in other comparable studies  (Jamnick & Beckett 1988 , Vasconcelos et al. 2001 , Martinez et al. 2009). The outputs of the logistic regression model were compared to the spatial patterns of the original forest fire database. The binary classification of municipalities in high or low danger was compared with the actual values of the original dataset, assessing overand under-estimation using 0. 5 as classification criterion. Underestimations would be the municipalities with predicted low ignition danger though actually having a high forest fire danger, while overestimation would be the opposite.
Results The best logistic model to analyze the influence of socio-economic factors on forest fire ignition has been selected out of the five models computed (Tab. 2 and Tab. 3) in order to identify the best combination of explaining variables. Railroad density (RAIL/ TOTAREA), density of forest roads (FOR- ) computed for the model building set with 1710 cases of "fire" and "no fire" events reached a total percentage of correctly predicted cases of 63. 4. The validation dataset (Tab. 5b) showed a slightly lower total value of 60. 5 %. Since the original database is relatively small (955 cases) and forest fires are spread over fairly large areas and long time periods, the results can be considered satisfactory. For both data sets reported in Tab. 5 better performances were obtained in identifying municipalities with lower fire danger (72.
8 % for the model building dataset and 63. 0 % for the validation dataset). Fig. 1  shows the probabilities distribution of the logistic model for the "fire" and "no fire" events from the model data set (Fig. 1a ) and the validation data set (Fig. 1b ). In the model data set, a left-sided distribution was obtained, whereas in the validation data set no clear tendency was observed. A sensitivity analysis has been performed in order to identify the influence of the significant variables included in the model. In Tab. 6 the six variables included in the model are ranked by a global score, obtained by adding the ranks of the five evaluation criteria: the lower is the ranking the more important is the variable in the model. The railroad, hiking trail and forest road density were found to be the most relevant factors independently from the chosen variable of significance.
Two maps have been generated with the selected model with the aim of illustrating model predictions for all municipalities which data were included in this analysis. Fig. 5  displays the spatial distribution of the forest fire danger at the level of municipalities, and Fig. 6  reports the model prediction for the forest areas only. The estimated probabilities have been classified in five fire danger classes. In Tab. 5 it can be observed that the majority of the forest areas is classified in the very low to medium fire danger classes (83. 8 %); only 16. 2 % of the forest area comprises a high to very high forest fire danger. Some 15. 5% of all fire ignitions observed are located in the very high and high danger classes, and the very low and low danger class comprises 21% of all ignitions observed.
Discussion The logistic regression carried out in this study provides a model based on a set of socio-economic factors potentially affecting forest fire ignition, in addition to the driving factors of climate and fuel. The logistic model obtained correctly classified 63. 1 % of the calibration and 59. 5 % of the validation dataset by using the midpoint (0. 5) of the logistic function, which is customarily used in most analyses  (Martinez et al. 2009 ). Depending on the fire management strategies and objectives, a different level of probability could be used to estimate forest fire danger. Comparing the medium to high fire danger areas of the model output with the known fire "hot spot" areas in Austria from a previous study (districts of Neunkirchen in Lower Austria, in Kapfenberg in Styria as well as the region around Villach and Arnoldstein/Spittal in Carinthia -  Vacik et al.
Although a high average density of forest fire ignitions per forest area occurs in the highest fire danger class, the probability of fire occurrence was found to be higher in districts further away from areas densely populated or with a high density of infrastructure. According to  Brosofske et al. (2007)  the explanation might be that arsonists seek especially remote areas to avoid capture. Moreover, the authors argue that the detection of forest fires might be lower in remote areas due to a lower density of infrastructure and therefore a generally low level of human activity in forests. These hypotheses might partly hold for the mountain forests in Austria. In general, a high level of fire detection is assumed as the average size of burned areas of most fires in the database is small. This assumption is supported by findings of  Cardille et al.
2002). First attempts using Geographic Weighted Regression have provided interesting results in the European Mediterranean  (Koutsias et al. 2005 (Koutsias et al. , 2010)). The logistic probabilities would allow human factors to be integrated with meteorological factors  (Arpaci et al. 2013 ) and fuel conditions. Statistical or physical models could be used for integration of factors in order to build an integrated fire danger model  (Martinez et al. 2009). So far, records on wildfires and forest fires have not been documented consistently by the respective agencies, municipalities and fire brigades in Austria. The dataset underlying this study has been established in the last four years and the process of data assembly is still ongoing. Similar to other studies, the dataset still contains uncertainties regarding the size and location of forest fires and it is still incomplete because of the decentralized documentation of fire records  (Vacik et al.
Tab. 5 -Classification table for model building dataset and validation dataset. Percentage of correctly classified: p=0. 5. Dataset Class Predicted No fire Fire % Correct (a) Model building data Observed No fire 622 233 72. 7 Fire 392 463 54. 2 Total percentage 63. 4 (b) Validation dataset Observed No fire 63 37 63 Fire 42 57 58 Total percentage 60. 5.
Evaluation of the fire danger map classification with the validation dataset. Change in -2LL (iv) Regression Coefficient B (v) Global Score (Sum) RAIL/TOTAREA 1 1 1 5 1 8 HIKETRAIL/TOTAREA 3 2 1 2 2 9 FORROAD/TOTAREA 2 3 1 3 3 11 AGRPAST 4 4 1 4 4 16 Tab. 7 -Fire danger classes Forest area (%) Ignitions observed (%) Average density (fires/100 km 2 ) Very low (0-0. 19) 24. 6 9 1. 16 Low (0. 2-0. 50) 31. 6 12 1. 2 Medium (0. 51-0. 65) 27. 6 13. 5 1. 54 High (0. 66-0. 87) 13. 2 12 2. 85 Very high (0. 88-1. 0) 3 3. 5 3. 71.