dataesr/scientific-paragraphs-categorization huggingface dataset:
EXPERIMENTAL RESULTS AND DISCUSSION In order to evaluate the performance of the proposed method, two different datasets were selected. 1) Mangrove forest dataset: This image was recorded by the QuickBird satellite over the mangrove forest in the south east of Iran, which in UTM projection is located between, ULX: 347949. 3, ULY: 2786168. 1 and LRX: 348126. 9 , LRY: 2785990. 5 on April 06, 2006. The studying area is located within Govatr Bay in the delta of Bahookalat River which is a part of Gandoo Protected Area and Bahoo wetland and demonstrated as position 10 in Figure  2   [Zahed et al. , 2010]. The satellite simultaneously captures panchromatic (Pan) and multispectral (MS) digital imagery with spatial resolution of 0. 6 and 2. 4 m at nadir, respectively. Thus, the scale factor S between MS (Figure  3(a) ) and Pan (Figure  3(b) ) images is 4.
The Pan sensor captures the surface reflectance within a wavelength range of 450 to 900 nm, while the MS sensor provides four spectral bands; i. e. blue (450-520 nm), green (520-600 nm), red (630-690 nm), near-IR (760-890 nm). The selected area comprises 300 by 300 panchromatic pixels and their corresponding multispectral pixels. In this dataset, we tried to select a pure studying area with some individual mangrove tree crowns, in the non-dense mangrove forest. Therefore, we were able to extract the trees accurately and evaluate the results. The reference map (Figure  3(c )) was produced using visual interpretation of the panchromatic image and pan sharpening product. Figure  2 : Govatr Bay mangorves forest; adopted from  (Zahed et al. , 2010)  2) Simulated image: This dataset is a simulated image which was used to compare the accuracy of our improved adaptive MRF-SRM with that of  [Li et al.
, 2013]. In order to generate this dataset, we employed the reference map of mangrove forest dataset comprising 300 by 300 pixels, which contains real tree boundaries. Then, the trees are categorized into two different classes (class 1 and class2) to generate a reference map with complex class conditions (Figure  5(a) ). The third class of this map is considered as soil. Then, we employed the  (Mohn et al. , 1987; Yu and Ekstrm, 2003)  methodology as well as the proposed class mean values and class covariance matrix. The location of each distribution is presented as an ellipsoid in Figure  4. Then by utilizing the mean and covariance of the classes, a synthetic image with two bands and three classes was generated by sampling from the multivariate normal distribution. The generated image is degraded by scale factor 6 (Figure  5(b  & c )) and utilized to apply adaptive MRF-SPM and our improved adaptive MRF-SRM.
To estimate the efficiency of the proposed spatially adaptive MRF-SPM  [Li et al. , 2012]  and our improved adaptive MRF-SRM method, we applied both methods on the both datasets. Then the overall (OA), average (AA) and class-specific accuracies, as well as the kappa coefficient (k) are estimated. The resulting generated SR maps for mangrove forest dataset are presented in Figure  3(d)  and for the simulated image in Figures  5(c   I , from which, it can be concluded that the improved adaptive MRF-SRM compared to the spatially adaptive MRF-SPM increased the overall accuracy by 5. 1 percent. Although all the pixel based classification methods were applied on the original simulated image with scale factor 1 instead of scale factor 6 which was utilized for both spatially adaptive MRF-SPM and improved adaptive MRF-SRM methods, the OA, AA, and k of both SR generated maps are higher than SAM, SCM and MiD classified maps results (Table  1 ).
The performance of this method was evaluated using a two band simulated image with scale factor 6 which contains three different classes with very complex shapes. The generated map using this method is compared with spatially adaptive MRF-SPM using the same dataset and five state-of-the-art pixel based classification methods for the original simulated image with a scale factor 1. Experimental results have demonstrated OA, AA, k of generated SR map by the improved adaptive MRF-SRM method are higher than those of spatially adaptive MRF-SPM. Moreover, McNemar's test rejects the null hypothesis (H 0 ) of no significant difference between them which indicates the statistically significant difference between them at the 0. 05 level.
These structures were calibrated using the concept of Pareto optimality with three criteria pertaining to streamflow simulations and one to the seasonal dynamics of snow processes. The results were analyzed in the four-dimensional space of performance measures using a fuzzy cmeans clustering technique and a differential split sample test, leading to identify 14 equally acceptable model hypotheses. A filtering approach was then applied to these best-performing structures in order to minimize the overall uncertainty envelope while maximizing the number of enclosed observations. This led to retain 8 model hypotheses as a representation of the minimum structural uncertainty that could be obtained with this modeling framework. Future work to better consider model predictive uncertainty should include a proper assessment of parameter equifinality and data errors, as well as the testing of new or refined hypotheses to allow for the use of additional auxiliary observations.
, 2010; Capell et al. , 2012)  or snow measurements  (Clark et al. , 2006; Parajka and Blöschl, 2008) , can also be used to improve the internal consistency of model structures. Additional criteria can then be introduced in relation to these auxiliary data or to specific aspects of the hydrograph (driven vs. nondriven components, rising limb, recession limbs, etc. ). In this perspective, multi-criteria evaluation techniques based on the concept of Pareto-optimality provide an interesting way to both reduce and quantify structural inadequacy  (Gupta et al. , 1998; Boyle et al. , 2000; Efstratiadis and Koutsoyiannis, 2010). A parameter set is said to be Pareto-optimal if it cannot be improved upon without degrading at least one of the objective criteria. In general, meaningful information on the origin of model deficiencies can be derived from the mapping of Pareto-optimal solutions in the space of performance measures (often called the Pareto front) and used to discriminate between several rival structures  (Lee et al.
Hydro-climatic data In order to represent the hydro-climatic variability over the catchment, a 30 year period   using the inverse distance weighted method on a 5 km × 5 km grid. Since very few measurements were available outside the river valleys, elevation effects on precipitation and temperature distribution were considered using the SRTM digital elevation model (Fig. 1 ). In a previous study,  Ruelland et al. (2014)  examined the sensitivity of the GR4j hydrological model to different ways of interpolating climate forcing on this basin. Their results showed that a dataset based on a constant lapse rate of 6. 5 • C km -1 for temperature and no elevation effects for precipitation provided slightly better simulations of the discharge over the last 30 years. However, since the current study also seeks to reproduce the seasonal dynamics of snow accumulation and melt, it was decided to rely on a mean monthly orographic gradient estimated from the precipitation observed series (Fig.
, 2014). Naturalized streamflow time series were estimated using information provided by the Chilean Dirección General de Aguas, mainly streamflow measurements at the gauging station of Rivadavia and historical surface-water diversion data. In addition to streamflow data, remotely-sensed data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor were used to estimate the seasonal dynamics of snow accumulation and melt processes over a 9 year period  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011). Daily snow cover products retrieved from NASA's Terra Figures.
6d ) are used. By contrast, options F2a (Fig. 6c ) and F1a (Fig. 6d ) require very different parameter sets to adequately simulate both low and high flows. Again, this suggests that runoff transformation options based on a threshold-like behavior may be more consistent with the observed data than those based on a power law relationship. It should be noted, however, that relatively large Pareto ranges in some runoff transformation parameters (e. g. K 2 and K 3 ) may still be required to obtain small volume and snow errors at the same time as high low-flow and high-flow performances (e. g. models no. 44 and 54). Interestingly, the black, red and blue solutions of models no. 49, 50, 53 and 54 also converge towards the same low values of parameter K C (evapotranspiration coefficient) independently of runoff transformation options.
e. being close to, but not part of, the Pareto front) that might have been Pareto-optimal with different performance measures, calibration data or errors in the input data (e. g. Freer et al. , 2003; Beven, 2006). One possible way to address this limitation and improve model transposability in time has been suggested by  Gharari et al. (2013). The idea is to divide the calibration period into k sub-periods and identify parameter sets (in the whole parameter space) which minimize the distance to the k Pareto fronts of these sub-periods. For a proper assessment of parameter equifinality, however, Bayesian frameworks should be considered  (Madsen, 2000; Huisman et al. , 2010). The use of Pareto-envelopes to quantify structural uncertainty is also questionable in that it fails to account for all discharge observations, as shown in Table  4.
g. ,  [11, 12, 13, 14]  but see  [15] ). However, most of these data are from individual marine reserves, or groups of reserves  [7, 8] , which are each compared separately. To date, there is little evidence that MPAs in a given network are performing synergistically. Properly designed networks of MPAs can theoretically outperform single marine reserves for a variety of ecological, economic, and social management goals. In theory, MPA networks can minimize the potential negative economic, social, and cultural impacts of a single large no-take reserve while producing similar or even greater ecological and economic returns from fishing outside the no-take areas (e. g. ,  [16, 17, 18] ). The International Union for Conservation of Nature's Marine Program defines a network as ''a collection of individual marine protected areas (MPAs) or reserves operating co-operatively and synergistically, at various spatial scales and with a range of protection levels that are designed to meet objectives that a single reserve cannot achieve''  [19].
On both islands, study sites were selected for the analysis based on similar monitoring methods during 2000-2003, except for Molokini, which included data from 1996-2004  [39]. After 2004, monitoring of the 'Ahihi-Kina'u and Molokini reserves ended, while sampling of the West Hawai'i MPAs is ongoing. At each island ''control'' study sites open to all fishing activities (nine in West Hawai'i and three on Maui) were monitored during the same time period (Figure  1 ). Sampling methods are detailed in Tissot et al. [39] , but briefly, fish densities for both yellow tang and potential predators were estimated via visual search by a pair of divers along four 2564 m strip transects per site. We estimated densities of top and mid-level predators using predator groups previously identified for Maui and West Hawai'i  [43] , excluding those that are known not to prey on Acanthurids such as yellow tang (i.
g. , West Hawai'i) or non-Network (e. g. , Maui) conditions, which is tested for a statistical difference from zero using a z-test. The results of these analyses for the West Hawai'i and Maui data show significant Net*BA and Net*MPA interactions (Table  2 ). These results support the hypothesis that MPAs on West Hawai'i had significantly greater percent change in density within MPAs and open areas before vs. after network establishment compared to the Maui non-network sites during the same period (Figure  2 ). Moreover, the network effect size was significantly greater than zero (Mean network effectiveness = 1. 6360. 69 [95% C. I. ], n = 18, z-test, p,0. 001). Overall, yellow tangs within marine reserves and open areas on Maui declined during the study period, while both MPAs and open areas in West Hawai'i increased during the same time period.
Although both marine reserves on Maui declined overall, abundances at 'Ahihi-Kina'u during the study period were high but variable and yellow tang were relatively less common at Molokini. Our data illustrate results consistent with a network effect that is greater than the sum of individual MPA effects. Although the West Hawai'i MPAs account for a greater area of protected coastline (35%) than the Maui marine reserves (,1%), a network effect is still indicated when the total area protected is standardized by response ratio (e. g. outside-inside comparisons). We compared protected sites that were both no-take for yellow tang, however the Maui sites were completely no-take marine reserves while some fishing for non-aquarium trade species is allowed in the West Hawai'i MPAs. To investigate whether differences in yellow tang predators may influence the densities of yellow tang across protection levels, we compared the top and mid-level predators by protection level (MPA, Open) and by island (Maui, West Hawai'i) (Table  3 ).
Acknowledgments We thank  S. Gaines  and  C. Costello  for formative discussions about the assessment of marine reserve networks as well as  M. Gomei  for useful conversations about social networks. Some of the Maui data were generously provided by  R. Sparks  and  S. Hau. This paper is an outcome of conversations that began at a symposium organized by the  Communication Partnership for Science and the Sea (COMPASS)  and the  Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO)  at the 2009  International Marine Conservation Congress in Washington, D. C. JC would like  to thank  EU   FP7 COCONET and biodivERsA BUFFER  for funding. This is contribution #  446  from PISCO, the  Partnership for Interdisciplinary Studies of Coastal Oceans.
Funding was provided by the  David and Lucille Packard Foundation  (http://www. packard. org/), Gordon and Betty Moore Foundation  (http://www. moore. org/), EU   FP7 COCONET  (http://www. coconet-fp7. eu/),biodivERsA BUFFER (http://www. biodiversa. org/518), Hawai'i Coral Reef Initiative Research Program (HCRI-RP , http://www. cop. noaa. gov/ecosystems/coralreefs/current/hcri-factsheet-cr. aspx)under the  National Oceanic and Atmospheric Administration (NOAA) , and  Coral Reef Monitoring Grants  under  NOAA 's  Coral Reef Conservation Program  (http://coralreef. noaa. gov/aboutcrcp/workwithus/funding/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGEMENTS We would like to thank  Annic Lefol-Malosse  and  Caroline Tricaud  for having developed the application technique;  Dr Catherine Busi , the dermatologist who evaluated the volunteers;  Ana de Lemos ,  Séverine Freyssinet , and  Jean-Christophe Paillard  for selecting the volunteers and the statistical data analysis; and  Patrick Göritz  for assistance with manuscript preparation.
Continuity Boundary Condition for Temperature at the Porous Medium-Free Medium Interface [39] In this work, we assume a local thermal equilibrium at the interface, providing a continuity boundary condition for temperature as   [40]  We also assume the continuity of the component mass fluxes across the interface. This equilibrium is considered as ½T ff 5½T pm ; on C (37) n: ðqc p Þ ' v ' T1ðqc p Þ g v g T 2K Ã rT h i pm 52 n: ðqc p Þ g ðT g v g Þ2K g rT g h i ff ; on C ( ½w v ff 5½w v pm ; on C (39) n: q g w v v g 2D Ã v rðq g w v Þ À Á Â Ã pm 52 n: q g w v v g 2D v rðq g w v Þ À Á Â Ã ff ; on C (40) [41] After the implementation of the initial and boundary conditions, the system of partial differential equations for the nonequilibrium model in a two-dimensional domain was simultaneously solved using the COMSOL Multiphysics software that is based on the Finite Element Method.
Five channel, continuous data loggers (Decagon Devices, Inc. Em50) were used to collect data from all the sensors. During the experiment, the tank was placed on a scale (Sartorius Model 11209-95, Range 5 65 kg, Resolution 5 61 g) in order to continuously monitor the cumulative weight loss of water from the tank. [46] To better understand the physical processes associated with evaporation in dry, bare soil conditions, heat flux, and air flow were induced over the soil surface to simulate an arid/semiarid environment. As shown in Figure  3b , heat and wind was generated by infrared heaters and a duct fan placed within galvanized steel ductwork (26. 0 cm by 8. 0 cm rectangular and 15. 2 cm round diameter, respectively). The ductwork was aligned along the centerline of the tank to channel heated air across the soil surface.
Wind velocity was monitored using a stainless steel pitot tube (Dwyer Instruments, Inc. Model 167-12, 0. 32 cm diameter, 30. 48 cm insertion length, accuracy 65%) connected to a differential pressure transmitter (Omega Engineering, Inc. Model PX653-0. 1D5V, Range 5 0-0. 1 inches of H 2 O, 0-25 Pascals) and an anemometer. The Pitot tube and anemometer were suspended in the center of the tank, 10 cm above the soil surface as seen in Figure  3a  for the entire duration of the experiment. The pressure data from the Pitot tube were converted into wind velocity in meters per second by solving Bernoulli's equation and compiled using LabVIEW software (National Instruments Corp. ). The wind velocity profile was used to obtain the average velocity over the soil surface.
Along each transect, the diver swam one way at constant speed (approximately 4 meters/min. ), identifying and recording the number and size of each fish encountered. Fish size (total length: TL) was recorded within 2-cm size classes for most of the species, and within 5-cm size classes for large-sized species such as Epinephelus marginatus. Fish wet mass was estimated from size data by means of length-weight relationships from the available literature  [36, 37]. The assessment of protection effects on fish assemblages can be influenced by habitat complexity  [38]. Substrate rugosity was measured in situ for each replicate transect, at all stations (both protected and unprotected), to account for variability in fish assemblages due to rugosity as a covariate. To measure rugosity, a 10-m long small-link chain (1.
3 cm per link) was draped along the length of the centerline of each transect. Care was taken to ensure that the chain followed the contour of all natural fixed surfaces directly below the transect centerline. A ratio of 10 to linear horizontal distance between the beginning of the transect and the end of the draped chain gave an index of rugosity (see  [14] ). We focused on variability in habitat complexity (i. e. rugosity) at a spatial scale that, from literature available, was shown to be relevant in structuring Mediterranean fish assemblages (see  [38]  for detailed discussion) and that matches the spatial scale of the sampling unit adopted in the present study (i. e. 25*5 m transect). To measure the level of protection, data from each area were pooled into the following three categories, in decreasing order of protection: (a) well-enforced no-take MPAs (HP: Highly Protected) (Formentera-Espardell, Medes, Portofino, Torre Guaceto, Tavolara), (b) MPAs where some fishing is allowed or some illegal fishing may occur due to weak enforcement (IP: Intermediate Protection) (Cabrera, Cap de Creus, Capo Caccia, Porto Cesareo, Cavalleria, Alonissos), (c) non-enforced MPAs (Piperi, Tremiti) plus 17 open access areas (F: Fished).
Data analyses The effects of different protection regimes on overall fish assemblage structure (i. e. taxonomic composition and relative contributions of fish taxa in terms of density or biomass) were analyzed using multivariate statistical techniques. Specifically, taxon x sample matrices (n = 82 taxa, n = 514 samples) were analyzed using unbalanced three-way permutational multivariate analysis of variance (PERMANOVA, Anderson 2001). 'Protection' (Pr) was considered as a fixed factor (3 levels, as classified  above), 'Site' (Si) was a random factor (up to 19 levels) nested in Pr, and 'Station' (St) was a random factor (up to 6 levels) nested in Si. The effects of different protection regimes on relevant fish community variables (i. e. taxon richness; total fish density and biomass; density and biomass of each commercial and trophic group) were analyzed using univariate techniques.
In order to assess potential patterns of taxon richness along both latitudinal and longitudinal gradients, we used polynomial regressions with a parabolic shaped relation  [42]. To explore whether the same taxa contributed to the response to protection over the regional scale, across different MPAs, PERMANOVA was also carried out on the 5 HP MPAs considering 'Site' (Si; 5 levels) and 'Station' (St; up to 6 levels) as random factors (up to 6 levels) nested in Si. To visualize multivariate patterns, non-metric multidimensional scaling (nMDS) ordinations were obtained from Bray-Curtis dissimilarity matrices where only the 5 centroids for the factor Site were visualized. Stress values were shown for each MDS plot to indicate the goodness of representation. The PRIMER 6 and Permanova+B20 package (Plymouth Marine Laboratory) was used to perform the analyses  [43].
Discussion Our region-wide survey of Mediterranean rocky reef fish assemblages clearly shows significantly higher fish biomass in notake MPAs relative to partially-protected MPAs and open access fishing areas. Partially-protected MPAs are closer to open access fishing areas along the recovery trajectory from unprotected areas to HP MPAs. These findings are generally consistent with the evidence arising from previous field studies dealing with single MPAs  [22, 44]  or meta-analyses using data from multiple MPAs  [4, 21, 33]. This is, however, the first field study showing MPA effects on fish at this large scale and using consistent methods and design. Thus these findings obtained in a field study highlight the generality of the effects of protection on reef fish assemblages, over spatial scales not addressed before.
The absence of any protection effect on fishes with null commercial value in our dataset could be due to the weakness of trophic interactions specifically involving piscivorous fishes and their fish prey  [46] [47] , possibly because large-sized piscivorous predators (e. g. seals, sharks, groupers, common dentex) are at low levels, relative to historical populations, along most Mediterranean coastal habitats  [13, 14, 48, 49]. High trophic levels (i. e. apex predators, carnivores) showed both higher density and biomass within HP MPAs than to in IP MPAs and fished areas. On the contrary, herbivorous and planktivorous fishes did not display any response related to protection from fishing, as previously highlighted by Guidetti and Sala  [4]. As a consequence, within HP MPAs the trophic structure of fish assemblages resembles a top-heavy (i.
It is challenging to determine which process has a larger effect because historical species arrival data and past ecological processes are usually unknown. When analyzing spatial datasets, striking and puzzling results are found if spatial autocorrelation is ignored because response variables are structured on various spatial scales  [15] [16] [17] [18]. Specific spatially explicit sampling protocols for targeted organisms and different approaches are needed in soil ecology studies  [17, 19, 20] , although these methods must be used with caution  [16, 21]. Geostatistics  [22]  allows the assessment of the spatial distribution of soil environmental variability and soil organisms  [22, 23] , but other powerful statistical tools are necessary to model spatial structures on various scales, such as principal coordinates of neighbor matrices (PCNM)  [3, 24, 25].