dataesr/scientific-paragraphs-categorization huggingface dataset:
GLCM-Derived AGB Estimates The GLCM method achieved strong to very strong positive correlations between SVR-predicted AGB and field-measured AGB for both the 50-cm resolution GE imagery and the 8-cm resolution aerial multispectral imagery, with r-values (Pearson's product-moment correlation coefficient) ranging from 0. 630 to 0. 888. Pearson's correlation was chosen in order to identify any relationship between the field-measured and SVR predicted AGB  [39]. As evident in Figures  1  and 2 , comparatively high r-values and low RMS errors are obtained from GE imagery compared to those from VHR imagery. For both datasets, the same trend can be observed; correlation coefficient (r) was highest and RMSE was lowest when both horizontal canopy variables (HCV) and vertical canopy height (VCH) were included in the analysis.
The Importance of Structural Parameters The present study employed several model combinations to estimate AGB stocks and their spatial distribution. Previous studies have utilized solely multispectral sources to estimate AGB  [10, 15] , although in the majority of the texture models-GLCM, FOTO and Gabor wavelets-the inclusion of vertical height data provided marginally improves AGB estimates. However, ground-measured canopy cover percentage was strongly associated with field AGB values (r = 0. 698). Previous studies in the tropics using different datasets, and modelling approaches have established the role of vertical canopy height in producing accurate estimates of AGB  [23]. However, the corresponding role of the percentage of canopy cover was not previously examined in the literature. While this study establishes the role of canopy cover in explaining AGB variation, there are no universally accepted protocols for measuring canopy cover, either in the field or using remote sensing data.
Thus, multispectral-only models derived from VHR Digital Globe imagery could play an important role in the repeated monitoring of tropical landscape-scale AGB stocks. Repeated monitoring is vital for accurate reporting, monitoring and verification of forest carbon stocks for conservation instruments, such as REDD+, to support enforcement and enable reliable accounting of ecosystem service provision  [43] , especially at sites and in ecosystems that are dynamic and affected by surrounding land use decisions and external stressors  [44]. However, there is a need to test these models in other ecosystems (notably, closedcanopy forests, mangroves) at different spatial scales, different plot sizes and with other VHR data to improve their general applicability. Finally, not much is known about the data quality of the different image sources present within the GE database, acquisition errors and pre-processing algorithms applied to them.
Acknowledgments We are grateful to the  APSARA National Authority  for permission to undertake field research in the Angkor Thom area, including  Bun Chamrouen  and  Ley Kimsy  for their assistance in the field. So Malay ,  Martin Polkinghorne ,  Son Rothaphum  and the  Robert Christie Foundation  made essential contributions to the logistical and administrative aspects of the field programme. We thank  PT McElhanney Indonesia  for their contribution to the LiDAR acquisition and acknowledge support and funding from the eight institutions of the  Khmer Archaeology LiDAR Consortium : the  APSARA Authority , the  University of Sydney , the  École française d'Extrême Orient ,  Société Concessionaire d'Aéroport , the  Hungarian Southeast Asian Research Institute, Japan-APSARA Safeguarding Angkor , the  Archaeology and Development Foundation  and the  World Monuments Fund.
In the multivariate factor analytical approach (MFA), only environmental concentrations and uncertainties of pollutants are used as input data and the model computes the factor profiles and the mass contributed by the factors. The CMB approach is sensitive to the selection of sources, their stability and the collinearity among them. Differences between the methods used to analyse the source and ambient samples may also impact the results. On the other hand, MFA models identify factors that have to be attributed to emission sources. For a more thorough discussion about the pros and cons of the two approaches see  Hopke (2010) ,  Watson et al. (2008)  and  Belis et al. (2013). Previous studies provided first estimates of the output variability by comparing the results of different RMs on the same dataset  (Hopke et al.
, 2006; Larsen et al. , 2008; Favez et al. , 2010; Viana et al. , 2008b; Pandolfi et al. , 2008). In the present work, carried out in the frame of FAIRMODE (Forum for Air Quality Modelling), intercomparison exercises aimed at quantitatively assessing the performance and the uncertainty of RMs by comparing the results reported from different practitioners on the same dataset using different RM techniques.
, 2015). The "ff tests" are the comparison among factor/sources attributed by participants to the same source category in all the solutions while "fr tests" refer to the comparison between reported factor/ sources and a reference value. The objective of the "performance tests" is to evaluate whether the source contribution estimates (SCEs) are coherent with a 50% standard uncertainty target value using the z-score performance indicator complemented by the z 0score and zeta-score indicators  (Thomson et al. , 2006; ISO 13528, 2005). In this study, SCE denotes the mass attributed to a source or factor in the results obtained with either CMB or MFA approaches. The methodology is fully described in the companion paper by  Belis et al. (2015)  and was implemented using the open source software R (and R-studio).
Source categories with less than five factors/sources were not evaluated and profiles attributed by participants to more than one category were tested in each of the proposed categories. Considering that source apportionment studies are mostly targeted at identifying and quantifying the typical sources in the studied area, the performance tests were conducted on the average SCE over the whole time window represented in every dataset. Moreover, the SCE time series were evaluated using the root mean square error normalised by the standard deviation/uncertainty of the reference value (RMSE u ), as discussed in  Belis et al. (2015). The intercomparison exercises were structured in two rounds involving 16 and 21 organizations respectively. In the first round, 22 results were reported and 25 were provided in the second one.
A real-world PM 2. 5 dataset collected in Saint Louis (USA) was used in Round 1 (Table  1 ). The dataset used for the intercomparison was developed by merging two datasets: one of inorganic species collected every day  (Lee et al. , 2006)  and one of organic species collected every sixth day over the same time window  (Jaeckels et al. , 2007). In the final dataset, the structure of the uncertainties of the different species was heterogeneous with differences between species deriving from the data treatment in the original datasets and variability within single species due to the different analytical batches that were necessary to cover the whole monitoring campaign. In addition, the uncertainty of organic tracers was complex to quantify due to the possible influence of atmospheric chemistry and radiation on the degradation of these compounds  (Galarneau, 2008; Hennigan et al.
, 2010). The site and time window in which the real-world dataset was collected was not revealed to the intercomparison participants. The dataset containing the concentrations of 44 species in 180 samples with their analytical uncertainties was distributed to participants together with the analytical parameters (uncertainty of the method and minimum detection limits) and the emission inventory of the study area. In Round 1, the following preliminary tests were performed: Pearson and SID between factor/source profiles, Pearson between log-transformed factor/source profiles, and Pearson between factor/source time trends. Only ff tests were accomplished in this round because of the absence of independent unbiased reference values. In the performance tests of Round 1, the SCE reference value for each source category was the average of the results reported by the participants.
The reference values were obtained by calculating the robust average (Analytical Methods Committee, 1989) using only the SCEs of source/factors that passed the preliminary tests (Table  2 ). In the second round, a synthetic dataset with known reference values that were unbiased and independent from the results reported by participants was used (Supplementary Material S1). The chemical species included in the synthetic dataset (Round 2) are reported in Table  1  and the procedure followed to generate it is given in  Belis et al. (2015). Since the site was not disclosed to participants, the emission inventory of the study area and a set of 23 local source profiles (more than one for every source category) were distributed to them in order to: a) provide the necessary information to create the input files for CMB models, and b) support the interpretation of the models' output.
Number of factor/sources There are different techniques to determine the number of sources (e. g. Henry et al. , 1984). The procedures followed by participants to determine the number of sources were based on multicriteria, the most common of which were: a) the impact of the number of factors on the model diagnostics, b) the stability of factor profiles across different models set up, and c) the physical meaning of the factor profiles and their comparability with source profiles from the literature. In Round 1, nine factor/sources per solution are reported on the average (Table  3 ). One half of the solutions identifies between six and ten factor/sources while six solutions report more than 10. An approximation of the expected number of factor/sources for this round is derived from the original solution of the inorganic dataset obtained using PMF  (Lee et al.
, 2006) , which identified 10 different source categories. In this round, the estimations of PMF and CMB are relatively close. In Round 2, more than half of the solutions report the exact number of factor/sources used to design the dataset (  8 ) and all the solutions, except one, report between six and nine factor/sources. The tests suggest that the reliability of the performance diagnostics influence the ability of the tools to establish the most suitable number of factor/sources. Often, unconstrained MFA tools rank far from the average. The higher number of factor/sources in COPREM is likely due to the attempt to apportion the secondary organic aerosols (not present in the synthetic dataset) and the split of ammonium sulphate into (NH 4 ) 2 SO 4 and (NH 4 )HSO 4. No relevant differences in the number of factor/sources are observed between CMB8.
Source categories with inhomogeneous chemical profiles, such as INDU, often present poorly correlated time trends suggesting that an imperfect separation and identification of the sources leads to a poor fit in both the chemical composition and the temporal pattern. Nevertheless, this general rule is not always valid. For instance, the time trends of SALT in Round 2 are quite comparable (Fig. 4 ) even though the chemical profiles of the factor/sources attributed to it are not homogeneous (Fig. 3 ). This apparent contradiction is explained by the high variance between the SALT time trends in the different reported results that is not detected by the Pearson test because the oscillations are synchronous. 3. 2. 1. 2. Contribution-to-species. The contributions of sources to the mass of every single species in the dataset expressed as percentage (contribution-to-species) were reported only in Round 2 (Fig.
Inaccuracy in the SCE estimation of DUST, SHIP and BRA have been associated with the lack of homogeneity in the chemical profiles of the source factors attributed to them, as pointed out in the preliminary tests. Alternatively, those factors/sources with poor scores in DIE and GAS are likely connected to results affected by the limited number of weekend days included in the dataset, as indicated by the preliminary test on time trends. The few z-scores of INDU ranking in the action area may be associated with divergences in both time trends and chemical profiles. In Round 1, about 80% of the reported factor/sources were obtained either with EPAPMF3, PMF2 or CMB8. 2. In each of these models, more than 80% of the z-scores are placed in the area of acceptance. Interpretation of the results of the other models should be made with caution due to the limited number of reported solutions obtained with them.
2 model performs a propagation of the input analytical uncertainty. Many practitioners using non-US EPA tools compute the output uncertainty with resampling and error propagation techniques in post-processing. The rotational ambiguity is not discussed in this section because only one of the used of tools (EPA PMF v5) was designed to estimate this kind of uncertainty. More discussion about the uncertainty test can be found in the companion paper by  Belis et al. (2015). The tests described in the previous sections were mostly oriented to assess: a) the bias by comparison with a reference value and b) the reproducibility intended as the range of results that can be obtained from a single dataset (with a given degree of noise) by different practitioners using the same or different tools. In the following, the analysis will focus on the assessment of the SCEs uncertainty estimation accomplished by RMs by comparing them with the one of the reference.
Considering that unbiased reference values are available only for the synthetic dataset, in this section are discussed only the results of Round 2. The mean of the reported relative standard uncertainties for the SCE of the whole time window in Round 2 is 13%. The lowest values are those in NO3 source categories and the highest are those in INDU. As for the models, the lowest uncertainties are those reported in ME-2 and CMB8. 2 solutions and the highest are those of COPREM solutions. No uncertainty was reported for the SCEs obtained with FA-MLRA. The uncertainty attributed to the reference was equivalent to the noise introduced in the synthetic dataset (20% standard deviation) that was derived from the analytical uncertainty in the input dataset  (Belis et al. , 2015). The zeta-score test indicates that a 68% of the declared factor/source SCE uncertainties are coherent with the one of the reference while a 19%, ranking in the action area, are likely underestimated (Fig.
2 showed quite satisfactory performance with successful z-scores ranging between 80% and 100%. The good agreement between CMB and PMF may be partially due to the main RM assumptions being substantially respected in the used datasets: limited alteration of the species between source and receptor and relatively stable source profiles. In addition, both types of tools account for the uncertainties in the input data, have built-in performance indicators and have been available long enough to allow a wide number of practitioners be familiar with them. For those models used in a limited number of solutions, only preliminary conclusions can be drawn at this stage. In general, fully unconstrained models which do not account for the input data uncertainty (e. g. FA-MLRA and APCFA) showed performances below the average.
This result is likely because in these tools, the noise deriving from the uncertainty structure of the datasets is incorporated into the factor/ sources  (Paatero and Hopke, 2003). The tests used to assess the SCE uncertainty reported in the solutions confirmed that the RMs output uncertainty estimation is coherent with the analytical/random uncertainty of the input data. Other components of the uncertainty could be evaluated in specially designed intercomparisons where RMs are either compared with other types of models or synthetic datasets with known perturbing factors are used. Processes altering the factor/ source chemical profiles could be detected in the preliminary tests by comparison with the reference source profiles. In addition, diagnostic ratios could be used to detect long-range transport or photochemical age of aerosols  (Hien et al.
, 2004; Decarlo et al. , 2010). The slightly better performance observed in Round 2 compared to Round 1 is likely connected to the differences between simulated and measured data. Round 1 was more challenging for the participants due to the inconsistencies in the uncertainties they had to deal with in a blind test with limited information about a non-European study area. On the contrary, the synthetic dataset contained internally consistent data with a lower level of noise and fewer source categories. In the real-world, the variability of profiles in time and the chemical reactivity of organic species may affect the source/receptor relationships. Datasets from areas with complex atmospheric transport and chemistry are likely more challenging for models to quantify the sources (especially secondary and/or distant ones) than areas influenced mainly by local sources.
The illumination was reflected to the objective O (Zeiss W Plan-Apochromat 63x/1. 0) with a filter cube (Semrock, FITC-3540C-000) of the microscope. Fluorescence images were acquired either with a CCD camera (Coolsnap ES 2 , Photometrics, Tucson, AZ, USA,) or a CMOS camera (Orca Flash, C11440, Hamamatsu, Massy, France). Illumination power was 2. 8 mW. The microscope and the CCD camera were controlled with the software Slidebook (Intelligent Imaging Innovations, Denver, CO, USA), the CMOS camera with the software HCImage (Hamamatsu). To obtain structured illumination, the galvanometric mirror was kept stationary; for uniform illumination it was vibrated with a frequency much faster than the frame rate. To this end the galvanometric mirror was vibrated with a sine wave (700 Hz) from a function generator (203047, Jeulin, Evreux, France).
Data analysis Images were acquired in 16 bit and exported in tiff format. All further data processing was performed with MATLAB (Mathworks, Natick, MA, USA). HiLo. Raw images were recorded alternatingly with uniform illumination and speckled illumination. Optically sectioned images ("HiLo images") were calculated from pairs of a speckled image (SWF) followed by a uniformly illuminated image (UWF) with the algorithm previously described  [12]. A plugin for Image J (Rasband, W. S. , ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej. nih. gov/ij/, 1997-2014) is available from the group of Jerome Mertz (http://biomicroscopy. bu. edu/). We generated custom made Matlab scripts based on initial code kindly provided by Daryl Lim and Jerome Mertz. We favored the Matlab implementation for greater control of parameters and for the possibility of automating data processing.
Acknowledgments We thank  Karl Kilborn  and  Glen from Intelligent Imaging Innovations  for their help in controlling the instrument with Slidebook,  Cathie Ventalon  for useful discussions on the HiLo algorithm optimization and help in the construction of the initial HiLo configuration,  Christophe Tourain  for custom-built electronics,  Marc Guillon  for helpful discussions,  Daryl Lim , and  Jerome Mertz  for providing the HiLo code. We are grateful to  David Ogden  for lending the galvanometric mirror. M. A. L. received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. PIEF-GA-2011-297917. E. R. acknowledges the " Fondation pour la Recherche Médicale ". This work received financial support from the  network Ecole des Neurosciences de Paris (ENP) , the  Atip/Avenir junior program  from  INSERM  and  CNRS , the  International Reintegration Grant  from  Marie   Curie Actions Framework Program 6 #   277200 , and the  European Research Council (ERC)  starter grant "OptoLoco" #  311673.
This work was supported by the  Human Frontier Science Program  ( RGP0013/2010 ), the  "Fondation pour la Recherche Médicale " through the program "FRM équipe", the  "Agence Nationale de la Recherche " (and  ANR-10-INSB-04-01 ,  France-BioImaging Infrastructure network ), the  FRC  and the  Rotary Club through the program  " Espoir en Tête " 2012 and the  National Institutes of Health  ( NIH 1-U01-NS090501-01 ).
Funding: M. A. L. received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. PIEF-GA-2011-297917. E. R. acknowledges the " Fondation pour la Recherche Médicale ". This work received financial support from the  network Ecole des Neurosciences de Paris (ENP) , the  Atip/Avenir junior program  from  INSERM  and  CNRS , the  International Reintegration Grant  from  Marie   Curie Actions Framework Program 6 #   277200 , and the  European Research Council (ERC)  starter grant "OptoLoco" #  311673. This work was supported by the  Human Frontier Science Program  ( RGP0013/2010 ), the " Fondation pour la Recherche Médicale" through the program  " FRM équipe " the  "Agence Nationale de la Recherche " (and  ANR-10-INSB-04-01 ,  France-BioImaging Infrastructure network ), the  FRC  and the  Rotary Club through the program  " Espoir en Tête " 2012 and the  National Institutes of Health  ( NIH 1-U01-NS090501-01 ).
Mechanistic model description. A full description of the GR3D model was provided by  [26]. The Java code is available online following this URL http://trac. clermont. cemagref. fr/ projets/SimAqualife/browser/GR3D_ECOMOD. The present work was conducted using this model. Here, we described the main model features and list all GR3D parameters in Table  1. GR3D combines population dynamics, repositioning behavior through dispersal process and climatic requirements to assess local and global persistence, as well as potential changes to the distribution of diadromous fishes in response to climate change over large spatial scales. GR3D has been designed to provide a wide variety of modelling applications ranging from applied questions-where it can be parameterized for real landscapes and species as in the present work-to more theoretical studies of species dynamics under different environmental pressures.
Data regarding the distribution of allis shad were obtained from the EuroDiad 3. 2 database (Irstea, Cestas, France; available at http://www. diadfish. org/; see S1 Table ). EuroDiad Physical environment and environmental data. The 197 river basins included in Euro-Diad 3. 2 were also described by key geomorphological attributes such as the coordinates at the outlet, the altitude of the source, the surface area of the drainage basin, and the length of the main watercourse (see S1 Table ). Basins were also characterized by their climatic conditions averaged across the period 1901-1910 by seasons (i. e. , winter: January, February and March; spring: April, May and June; summer: July, August and September; fall: October, November and December) and for the whole year. Historical near-surface atmospheric temperature at the outlet and precipitation across the whole basin were extracted from the recently up-dated CRU TS 3.
22 database  [58]  which comprises monthly grids of observed climate for the period 1901-2013, covering the global land surface at 0. 5 degree resolution (freely downloadable at http:// www. cru. uea. ac. uk/cru/data/hrg/). Seasonal and annual mean air temperatures were converted into water temperatures by assuming a basic linear relationship between these two variables with water temperatures being 2°C lower than air temperatures. For the mechanistic approach, all the basins in EuroDiad 3. 2 could not be taken into account because of the time consuming computational processes required. Instead, 73 basins corresponding to the core distribution range of allis shad in northwestern Europe were retained to define the physical environment of GR3D. Retained basins were located along a latitudinal gradient between the Guadalquivir River in Spain (37°N) and the Drammenselva basin in Norway  (.