dataesr/scientific-paragraphs-categorization huggingface dataset:
The IRGA was connected to a computer interface, which allows recording the pCO 2 every second. Values were recorded during 24 h continuously. Eq. (  1 ) Where F was the CO 2 flux from water (µmol m -2 s -1 ) and converted in mmol m -2 d -1 ; k 600, was gas transfer velocity of CO 2 or piston velocity (cm h -1 ), and was calculated according to a wind speed function (k vs. U 10 ). K 600 was calculated using the equation from  Raymond and Cole (2001) , which developed from a data set of different estuaries and rivers as follow: k 600 = 1. 91 × exp(0. 35 × U 10 ) × (Sc / 600) -0. 5 Eq. (  2 ) Where U 10 was the wind speed (m s -1 ) at 10 m height above the surface water (was calculate from the wind speed (m s -1 ) at 2 m height, ("Appendix 8. 6-Wind Speed Calculations" 2010); and Sc was the Schmidt number, normalized to Schmidt number of 600  (Cole and Caraco, 1998).
, 2008; Sun et al. , 2010). In this study, DIC contents were calculated from the sum of including HCO 3 -, CO 3 2-, H 2 CO 3 and CO 2 contents, which were given by the calculation from the CO 2 -SYS Software (version 2. 0) basing on the total alkalinity contents and pH values measured in-situ as described above (Sect. 2. 3). In order to compare with pCO 2 in water, which was measured by the equilibrator, pCO 2 in water at the 5 sites was also simultaneously calculated on the CO2-SYS EXCEL Macro Software basing on total alkalinity contents and pH values measured in-situ at the 5 sites as described above (Sect. 2. 3). We feel confident about our measurements of pCO 2 , because both methods yielded similar results.
Comparisons of the pCO 2 results for the two methods pCO 2 along the lower Red River (Vietnam) in the dry and monsoon seasons were determined by two methods: i) direct measurements using an equilibrator connected to an IRGA, ii) calculated from pH and alkalinity using the CO 2 -SYS ® software. The direct measurements gave higher values than the calculated (Table  2 ), but the values of two methods were well correlated (R 2 = 0. 76, Fig. 3 ). This result Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-505 Manuscript under review for journal Biogeosciences Discussion started: 13 December 2017 c Author(s) 2017. CC BY 4. 0 License. is opposite to what was observed in organic-rich freshwater  (Abril et al. , 2015)  or in estuarine mangrove waters  (Leopold et al. , 2017) , where large overestimation of the calculated pCO 2 was observed.
CO 2 flux out-gassing in some World Rivers. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-505 Manuscript under review for journal Biogeosciences Discussion started: 13 December 2017 c Author(s) 2017. CC BY 4. 0 License. calculated the values for the pCO 2 Water-Air Gradient (∆pCO 2 in µatm) nd. No data Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-505 Manuscript under review for journal Biogeosciences Discussion started: 13 December 2017 c Author(s) 2017. CC BY 4. 0 License.
Acknowledgements This work was performed in the framework of the  ARCP2014-03CMY-Quynh/ARCP2013-06CMY-Quynh/ARCP2012-11MY-Quynh  and the  Vietnam-NAFOSTED   105. 09-2012. 10projects. The authors would like to thank and the  Asia-Pacific Network for Global Change Research (APN)  and the  Vietnam's National Foundation for Science and Technology Development (NAFOSTED-Vietnam)  for their financial supports. We thank  Ms Nguyen Bich Ngoc  and  Mr Nguyen Trung Kien  for helping field work.
Atmospheric forcing This study makes use of the latest ensemble of high-resolution climate models from CORDEX  (Jacob et al. 2014)  which uses an ensemble of GCMs from CMIP5  (Taylor et al. 2012). A subset of 11 projections was chosen to represent the spread from the full CORDEX ensemble using cluster analysis  (Moss et al. 2010). They are based on five unique GCM/RCM combinations (four GCMs and four RCMs) and three  RCPs (RCP2. 6, 4. 5 and 8. 5) , and these were combined in different ensembles for the different warming levels. To drive the hydrological models, the climate projections of precipitation and temperature were bias-corrected to the E-OBS  (Haylock et al. 2008 ) gridded observation database using quantile mapping  (Wilcke et al. 2013). Other forcing variables were bias-corrected using WFDEI  (Weedon et al.
Acknowledgements The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under the project: IMPACT2C: Quantifying projected impacts under 2 °C warming, grant agreement no. 282746. We are grateful for use of the regional downscaling and bias-correction done within that project. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
It is equipped with a 4 mm lens and a 6. 5 x 5 mm sensor capable of producing 12 MP images. In addition to the UAVs, a standard DSLR camera was also used to complement the data acquisition. This is due to the existence of vegetations (trees and shrubs) around the church, which hindered aerial images of the lower parts of the building to be taken. The DSLR camera used was a Nikon D3200 with 24 MP images. The lens used had a focal length of 24 mm. The camera to object distance was maintained in such way as to imitate the GSD (Ground Sampling Distance) of the UAV images. A time-of-flight TLS was also employed in this project in order to obtain reference data with which comparisons can be made with the photogrammetry data. In this case, a Trimble TX8 laser scanner was used to obtain point clouds of the church's main façade and its South-East façade.
As regards to the software solutions tested, Photoscan (Agisoft), Photomodeler (EosSystems), and Pix4D were used from the commercial side. From the open source domain, the Apero-Micmac software suite developed by IGN (France) was also tested. For further use in this article, Pix4D will be abbreviated as P4D, Photoscan as PS, Photomodeler as PM, and Apero-Micmac as (AP-MM). Pix4D (P4D) and Photoscan (PS) are two commercial solutions with a rather black-box nature. However, concurrent with the results of  Remondino et al. (2014) , PS most probably performs a modified SGM (Semi-Global Matching)  (Hirschmüller, 2011)  of stereo pairs to generate depth maps, and then employs epipolar constraints at the end of this process to filter the results. P4D may have used a similar albeit different approach, since an SGM-based matching is offered as an additional plugin.
Photomodeler (PM) has long been used by the architecture and archeology community for performing 3D image-based measurements  (Grussenmeyer et al. , 2002). This software has an advantage over other commercial solutions in that it provides more statistical information on its results, making it less blackbox like. PM has since added a dense matching module based on stereo-pairs and more recently on a multi-view geometry. Meanwhile, Micmac (MM) uses a multi-resolution and multiimage approach to dense matching (Pierrot-  Deseilligny and Paparoditis, 2006). MM is modular and works with several levels of complexity and automation. The first module, Pastis, searches and matches tie points on the images. Apero (AP) performs bundle block adjustment to retrieve the external orientation parameters of the camera stations.
The results can be seen in Tables  2  and 3 , where the units for the principal distance and the principal point offsets are in millimeters and P4D* denotes the values of the calibration parameters given by the P4D database. The plotted radial distortion curves can also be seen in Figure  2. 3. Calibration results for the Albris In the case of the Phantom 3, a small systematic error was observed on the principal distance and principal point offset values. A systematic difference between 0. 1 and 0. 2 mm were detected for these three parameters, in comparing the computed values from the various algorithms with the ones given by the P4D database (P4D* values). On the other hand, the Albris only produced a very small difference of 0. 04 mm for these parameters. As regards to the radial distortion coefficients, the Phantom 3 radial distortion curve showed an anomaly.
Aerotriangulation Two data sets were obtained on the St-Pierre-le-Jeune church. The first was the main façade data set, which was acquired using the Albris. By benefiting from its ultrasonic sensors, the camera to object distance in this data set was set to a constant of 8 meters in an effort to keep the GSD from changing. This yielded a theoretical GSD of 1. 4 mm. Also, the perpendicular plus oblique acquisition method was applied in this case. The rest of the church was imaged using the Phantom 3 with camera to object distances, which range from 10 to 15 meters (hence a GSD of around 4 to 6 mm). Phantom 3 did not have the ability to maintain its camera to object distance, so it was up to the pilot to approximate this parameter on the field. Analysis on the quality of the exterior orientation or aerotriangulation was performed on the two data sets.
The first data set was the main façade, which was acquired using the Albris (with 11 control points scattered on the façade). On the contrary only a portion of the rest of the church acquired by the Phantom 3 was analyzed (with 9 control points). The Albris aerotriangulation quality was tested using all four solutions. The differences between the calculated and measured coordinates of the control points were averaged. The resulting values from the four algorithms give on average an error of 7. 5 mm. It should be noted that since no coded targets were placed on the object, each control point measurement was performed independently in each software packages. This may lead to influences of accidental error on the differing results. However, it is interesting to note is that this value is much higher than expected.
By taking into account sub-pixel measurement and accidental errors, an approximation to the expected aerotriangulation error was obtained at 0. 6 times the average GSD. This gave an expected aerotriangulation error for this data set of 0. 84 mm. The calculated value is therefore almost 9 times higher. It should be noted that the Albris images were plagued by noises (an issue which had been acknowledged by Sensefly and is to be addressed soon), which hampered a proper sub-pixel marking of the control points. This led to worse manual measurement of the control points, and certainly contributed to this overestimation of the aerotriangulation result. On the contrary, the Phantom 3 dataset had an average theoretical GSD of 6 mm and an expected aerotriangulation error of 3. 6 mm. The obtained result gave an average error of 13 mm between PS and AP.
Dense Matching The dense matching was performed using all four algorithms in order to compare their performance. The comparison of dense matching parameters in each algorithm can be seen in Tables  4  and 5. The main façade (the Albris dataset) comprises approximately 500 images while the rest of the building (the Phantom 3 dataset) amounted to approximately 2,000 images. In addition, around 200 terrestrial images were also taken. Since different sensors were used to record the building, processing was performed independently for each data source (Albris, Phantom 3, and also the complementing terrestrial images). The resulting dense point clouds were then combined and meshed as a single entity. This was done in order to reduce the processing requirements, which can be very high for such a project with more than 2,700 images.
Therefore care should also be taken in interpreting these results. Results which are too precise might indicate an interpolating algorithm hidden behind the photogrammetric software or the presence of high accidental error. In any case, PS, P4D, and MM gave similar results in terms of standard deviation while PM once again suffered from noises as shown by its high standard deviation value (1. 6 cm). For the dataset imaged using the Phantom 3 UAV, the South-Eastern façade (Figure  7 ) shows a comparable result in terms of average error for all algorithms tested. Note that the lack of data on the roof beneath the gargoyles is due to the absence of sufficient points from the terrestrial laser scanner. When compared to the results of the previous comparison, a slight systematic error may have been present on the Albris dataset.
However in terms of standard deviation, all algorithms gave values of around 2 cm. This may be linked to the resolution of the Phantom 3 images. It is interesting to note that the standard deviation for the Albris and Phantom 3 datasets are of the same order, even though the Albris is supposed to have 38 MP in image resolution compared to the Phantom 3's 12 MP images. PM again generated the most noise, but this time its standard deviation value is comparable to that of PS. However, visually PS gave a more homogeneous point cloud. This may indicate a post-processing stage in PS which refines the generated point cloud. A more detailed view can be seen on the comparison of the St. John's gargoyle sample (Figure  8 ). In this sample, the mean error is again roughly comparable between the four algorithms.
The standard deviation also corresponds with the previously compared South-Eastern façade. It should be noted that the gargoyle was processed in the same time as façade. A more LoD-oriented processing workflow can well be implemented in this type of case, where the architectural detail is processed in a higher resolution than the rest of the global façade. All four software managed to process both UAV's dataset to reach a millimetric (in some cases even sub-millimetric) error in terms of dense matching. The supposedly high resolution of Albris images is however hampered by noises as evidenced by the centimetric standard deviation values constant throughout the results. Phantom 3 on the other hand is limited by its low resolution images in this regard. PM constantly gives high values of accuracy, but accompanied by a higher amount of noise.
MM in its C3DC mode is most of the time comparable to the other solutions, but its signal to noise parameter remains to be determined manually for each case to avoid too much holes. P4D managed to generate the best result for the Albris dataset (but not so with the Phantom 3 dataset). This may indicate the presence of corrections in regard to Albris images processed in P4D. Finally, it is interesting to note the high value of standard deviation in PS despite the homogeneous visual result. Suspicion is given to a hidden post-processing interpolation and/or extrapolation of the dense point cloud (see also  Murtiyoso et al. (2016)  and  Lachat et al. (2017) ).
ACKNOWLEDGEMENTS The research presented in this paper is the result of collaboration between  INSA Strasbourg's Photogrammetry and Geomatics Group  and the  Drone Alsace company  (www. dronealsace. fr/). The final 3D model (meshed and textured) of the St-Pierre-le-Jeune church can be viewed through the following link: https://skfb. ly/PtQF(last accessed  20 January 2017 ).
It is worth noting that the ELE site in Elbu Bay was previously studied in 2009  21. First, in order to select comparable study sites, two observers (AC, JP) systematically explored by kayak, snorkeling and free-diving the shallow (1-10 meters) habitats along the 5 km coastline of Elbu Bay and the 5 km coastline of la Revellata Bay (Fig. 1 ). We schematically mapped the micro-habitats of the entire explored area (substrate type, biotic cover, slope, depth)  24. This micro-habitat localization was completed through personal communications with Mediterranean phycologists or experts (K. Ballesteros, J. -M. Dominici, P. Lejeune, C. Pelaprat, and A. Chery). On the basis of this data we randomly selected for each locality a set of sites each containing both habitat types, i. e. wide C. balearica forests and DS bushland (as described in Cheminée et al.
Sampling was performed during the known settlement or post-settlement period of many Mediterranean species  [26] [27] [28] [29]. This period also follows the annual biomass maximum for most subtidal macroalgal assemblages and more specifically Cystoseira species  23, 30. According to previous studies  21, 27  , the juvenile fish families we expected in these habitats belonged mostly to the Labridae and Serranidae families. Nevertheless, our sampling took into account all necto-benthic and crypto-benthic juvenile species encountered. This sampling method was well-suited to our objectives since the studied species are sedentary when juvenile, and similar size and numbers of replicates were proven to provide accurate density data in other works  17, 21, 24, 25. The total length (TL) of individuals ( ± 0.
Resemblance measure matrixes were calculated from the initial data matrix containing for each sample the abundance of juveniles for each species. Analyses were based (for multivariate data) on the binomial deviance (scaled) dissimilarity measure or (for univariate data) on Euclidian distances  40. P-values were obtained by 999 permutations of residuals under a reduced model. Monte Carlo P-values were considered when there were not enough possible permutations (<200). Effects on juvenile assemblage composition were illustrated through a multivariate exploratory approach using a non-metric multi-dimensional scaling (nMDS). nMDS represents samples as points in low-dimensional space in such a way that the relative distance apart of all points are in the same rank order as the relative dissimilarities of the samples  41.
Acknowledgements This work was part of A. C. 's PhD thesis and was conducted within the scope of the  FOREFISH  Project funded by the  Total Foundation , from which we would like to thank  Laure Fournier  for her continued support. We are grateful to the teams of the  Scandola Natural Reserve  and the STARESO research station, who provided advice and invaluable support during field work in Corsica. Thanks to  E. Ballesteros  for his advice on study site selection and to  M. Harmelin-Vivien  and  J. -G. Harmelin  for helpful discussions and shared dives during our Scandola field work. Authors wish to thank  M. Paul  and  M. Rider  for revision of the English text.
Simulations were carried out for the period 1989-2015 using the SAFRAN meteorological forcing system. The simulations were compared using four observation datasets including point snow depth measurements, seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite sensors, and the annual equilibrium-line altitude of glacier zones, derived from satellite images. The results showed that in both approaches the Crocus snowpack model effectively reproduced the snowpack distribution over the study period. Slightly better results were obtained using the distributed approach because it included the effects of shadows and terrain characteristics.
, 1993). This provides the atmospheric variables needed to run ISBA-Crocus, including air temperature, specific humidity, long wave radiation, direct and diffuse short wave radiation, wind speed, and precipitation phase and rate. SAFRAN was specifically developed to provide meteorological forcing for mountain areas at a suitable elevational resolution. The SAFRAN analysis combines observational data obtained from automatic weather stations with manual observations with the guess from the global numerical weather prediction system ARPEGE  (Courtier and Thépaut, 1994). We used SAFRAN re-analysis, which benefitted from meteorological observations not available in real time  (Durand et al. , 2009a (Durand et al. , , 2009b ). This analysis system can provide outputs for punctual simulations, or semi-distributed outputs.
In the first case the analysis is performed directly for the elevations of the stations involved, while in the second case the analysis is performed for 300-m elevation bands. In both cases the spatial extent of the analysis is approximately 1000 km². These regions (known as "massifs") were defined by  Durand et al. (1993)  who took climatic homogeneity into account. In this study the SAFRAN analysis was only used for that part of the Mont Blanc "massif" which covers the entire study catchment. SAFRAN and SURFEX/ISBA-Crocus (hereafter SAFRAN-Crocus) are used in avalanche hazard forecasting in France, using the S2M chain  (Lafaysse et al. , 2013) ; this takes account of the altitude, aspect, and slope classes (semi-distributed simulation). The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017.
3. 3). In a final stage the snowpack semi-distributed simulations were assigned or re-projected onto the pixels of the study area DEM (the same DEM used for distributed simulations; 250x250 m grid size). The pixels were categorized according to the semi-distributed terrain classes: slopes from 0 to 10° were considered flat, those from 11 to 30° were assigned to the 20° slope class, and those > 30. 1° were assigned to the 40° class. From this categorization of the DEM the snowpack simulation outputs were assigned to each terrain class for all time steps. Thereby, for each time step a snow and ice distribution map was generated that spatially distributed the semi-distributed snowpack simulation obtained for the various terrain classes. This enabled comparison of the two approaches based on the same observation dataset.
Distributed simulation The distributed snowpack simulations were performed in a DEM having a 250x250 m grid spacing and covering the 205 km 2 of the study area. As SAFRAN reanalysis The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-184 Manuscript under review for journal The Cryosphere Discussion started: 27 September 2017 c Author(s) 2017. CC BY 4. 0 License. provides semi-distributed outputs, the meteorological forcing at hourly time steps was spatially distributed over the 250-m grid DEM using specific routines that accounted for the topographic characteristics of each grid cell, based on interpolated meteorological variables for the closest terrain classes  (Vionnet et al. , 2016). However, the meteorological model used was the same for all simulations, and only minor differences occurred because of the need to include the topographic characteristics of each pixel.
Evaluation strategy The availability of direct snow and ice observations for mountain areas is limited. Broadly, when the time between observations is short, the spatial extent is limited and oppositely, when large areas are observed, the temporal frequency is low. Consequently, evaluation of the performance of a model in reproducing the snowpack evolution is difficult because of a lack of information. Although we did not evaluate a hydrological model in this study, the "observation scale" defined by  Blöschl and Sivapalan (1995)  aided assessment of the representativeness of the available observations. The observation scale is defined by: i) the spatial/temporal extent (coverage) of a dataset; ii) the spacing (space and time resolution) between samples; and iii) the integration volume (time) of a sample (also known as support).
These three criteria can rarely be optimized simultaneously. Hanzer et al. (2016)   The four datasets used in evaluation of the simulations are described below. Not all simulations (punctual, semi-distributed, and distributed) were evaluated using all four observation datasets. The punctual snow depth simulations only provided a preliminary evaluation of the simulation setup in terms of reproducing the temporal snowpack evolution, so only punctual snow depth observations were used in the evaluation of this simulation approach. The three other datasets (SCA, and glacier SMB and ELA) were used in evaluating the semi-distributed and distributed simulations, as these datasets had the appropriate spatial and temporal extents needed to assess the performance of these two approaches.