dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent computational advances, namely the availability of GFlow  49  and the new implementation of Circuitscape 52 in Julia, have overcome previous computational limitations of Circuitscape and provide an important opportunity for developing and testing large-scale connectivity models based on current density. Here we (i) propose a conceptual framework for species-agnostic connectivity modelling based on current density; (ii) discuss conceptual and computational decisions involved in implementing the approach; and (iii) assess the degree of uncertainty related to these decisions. The applied goal is to derive one main wall-to-wall map of current density as a measure of species-agnostic connectivity for Alberta based on the degree of human modification. The province of Alberta, Canada, is aiming to integrate an index of landscape connectivity into their biodiversity and ecosystem services assessment framework  53.
It is important to acknowledge that even the use of a species-agnostic model implies assumptions about organismal movement, albeit in a generalized way. The decision of how to treat natural elements like water or slope has been discussed above. An implicit assumption is also that human landscape features affect most species in a similar way. However, examples from the literature indicate, e. g. , that some species may actively use roads as corridors, whereas for other species, forest may impede movement  64. Acceptance of species-agnostic models of landscape connectivity will likely depend on validation with independent ecological data, as successfully performed by Koen et al. 28  for a limited set of species and at a smaller spatial scale. While testing the different models is beyond the scope of the present paper, we plan to use the unique biodiversity monitoring data (with 1656 sites spaced 20 km across Alberta; ABMI) and derived species-level distribution models available through the ABMI to test our connectivity maps.
Note that a large-scale connectivity map is not designed to evaluate connectivity in a network of patches (e. g. , a natural heritage system). To evaluate connectivity between two specific patches, considering all possible movement paths by using current density, nodes would have to be placed in the two patches  42. In a network model, it is possible to quantify the contribution of each patch or link to overall probability of connectivity  65, 66. However, this should not be done based on current density, as such network analysis aims to distinguish between alternative paths, and hence least cost path distances are better suited for this goal. Linkages between pairs of patches can then be modelled probabilistically based on assumptions about organism's dispersal ability  50. Large-scale, high-resolution modelling of current density remains a computational challenge, despite the new, computationally efficient implementation of Circuitscape in Julia  52  or the alternative implementation in GFlow  49.
Spatial human footprint data. To map landscape structural connectivity in Alberta as current density, we used ABMI's Human Footprint Inventory (HFI) derived from the detailed 2014 land-use dataset (for complete information on data collection and computation of the land-use geodatabase, see Geospatial Centre ABMI, 2018). This dataset was derived from 2014 SPOT6 Satellite Imagery (1. 5 m Color SPOT 6 Mosaic) provided by Alberta Environment and Sustainable Resource Development, Informatics Branch (https://www. alberta. ca/environment-and-parks. aspx). The original geodatabase consisted of 21 polygon-based layers including 115 land-uses grouped into 6 main categories: agricultural, forestry, transportation, urban, energy, and human-created water bodies  71. To avoid boundary effects  72  , we mapped human footprints in a buffer zone surrounding Alberta, using several datasets collected at the federal levels of Canada and the USA.
Land cover in the Canadian buffer zone was mapped using raster thematic data originating from classified Landsat 5 and Landsat 7 ortho-images circa 2010 (resolution: 30 meters)  73. The physical footprint of roads and railways in the Canadian part of the buffer zone was mapped using the National Road and Railways Network databases  74, 75. Land cover in the USA buffer zone was characterized using the National Land Cover Database 2011 for the conterminous United States (NLCD  76  ; www. mrlc. gov). The physical footprint of roads and railways in the USA was mapped using TIGER 2014 data (http:// www. census. gov/geo/maps-data/data/tiger-line. html). After homogenizing and grouping variables for each dataset, we retained 84 human-footprint categories in total, with 66 categories for Alberta, 8 for the Canadian buffer zone, and 10 for the USA (Appendix 1).
Percent slope s was derived from the US National Elevation Dataset for the entire study area  78. We modified Eq. 2a for the main map to assign water bodies a lower resistance value of 57. 7, which corresponds to an intermediate value of H FU = 0. 5 (Eq. 2b): = + + +. * R H s w ( 1 ) 4 57 7 (2b) FU 10 For the sensitivity analysis, we independently varied the H index (H F or H U ), the scaling function, and resistance of water bodies in Eq. 2a as shown in Table  3 , resulting in eight combinations. Raising (H + 1) to the power of 10 results in a very strong contrast between natural and non-natural landscape features  34. As a low-contrast alternative, we used a linear scaling function, (1 + H * 1000), which preserves the proportional importance of human modification values. This means that for instance, resistance values R based on H F with low-contrast scaling of H and low resistance of water bodies w were calculated as: = + * + + * R s w (1 H 1000) 4 0 (2c).
F Because of computational limitations, we aggregated the 10 × 10 m resolution resistance layers R to 100 × 100 m resolution before assessing current density. Cells were aggregated using the mean resistance value of combined 10 × 10 m cells (aggregate function in R, raster package  79  ). General procedure for current density computation. We derived a current density map from each resistance map R at the 100 × 100 m resolution using GFlow  49. GFlow is a new software which parallelizes the computation of circuit theory  42  and allows for simultaneous large-extent and fine-grained connectivity modelling. We simulated current density between pairs of points (nodes) randomly chosen at each iteration among 50 points that were evenly distributed (approx. every 100 km) along the outer margin of the buffer zone (Fig.
All four matrices were converted to dissimilarity matrices. For each dissimilarity matrix, we performed a db-RDA  82  where the three explanatory factors (H index, scaling function, and water resistance) were coded as dummy variables. We used the function varpart in the R package vegan to obtain adjusted R 2 values for the contribution of each factor. Unless otherwise stated, all computations and analyses were performed in R 3. 4. 2  83 on Niagara supercomputer at the SciNet HPC Consortium. Water resistance Low 0 High 1000 Table  3. Independent factors and levels considered in the sensitivity analysis and their levels, and their associated parameter values.
Acknowledgements We thank  Paul Leonard  for his help with implementing and using GFlow. We thank  David Theobald  for his advices on applying his methodology. We thank the  ABMI's Application Centre (Ecosystem Services Assessment Project  funded by the  Alberta Innovates : Grant #  BIO-12-006 ); and  ABMI's Geospatial Centre especially Carrie Selin  and the HF mapping team members. We thank the  AHFMP Technical Committee Members  for their help on defining intensity values of human-footprint categories. RM and HAM were funded through the  ABMI Ecosystem Services Assessment Project  funded by the  Alberta Innovates  (Grant #  BIO-12-006 ) and the  ABMI Human Footprint Inventory Program. HAM was also funded by the  Centre for Urban Environments (CUE) postdoctoral grant. The project was further supported by  HW 's  NSERC   Discovery grant.
Here we report on saturations of shallow gas hydrates deduced from shallow pressure coring at Pockmark A and discuss hydrate-related mechanisms affecting its sea floor morphology. Methane injection rates, time scales for hydrate formation, and evolution of the SMI were estimated by independently modeling the distributions of pore water chloride and sulfate in up to 57 m-long cores collected with the robotic sea floor drill rig MARUM-MeBo70  (Freudenthal & Wefer, 2013). A synoptic interpretation of these data from different methods enabled to provide a comprehensive picture on the interplay between last fluid migration events, gas hydrate formation and general evolution of developed hydrate-bearing pockmarks.
, 2014). However, the impact of the presence of free methane gas on the chloride diffusion in the pore space is considered insignificant as these two compounds do not react with each other. The modeling approach adopted in this study does not include such likely transient occurrence of free methane gas and should be deemed as a simplified model. Precipitation or dissolution of gas hydrates is assumed to happen immediately in the model, when the conditions are satisfied; that is, no kinetic barriers exist. Therefore, the extent of methane solubility (MSol), a function of temperature, pressure, and salinity, is the only factor to determine hydrate dynamics. The local gas hydrate equilibrium (GHEQ) and MSol were derived by using the CSMGem program  (Sloan & Koh, 2007)  with information of temperature, pressure, and salinity from the investigated sites (see Text S1 and Figure  S1 ).
Sulfate Concentrations The depth profiles of sulfate concentration indicate that in the shallow sediment of the pockmark, which is of relevance for this study, methane is supplied from the ToGH. This assessment is supported by the relatively short distance between the ToGH and the SMI (Table  1 ). Because lateral fluid migration was not observed in the interval between the ToGH and the SMI, the sulfate concentration profiles can be simulated by a 1-D transport-reaction model. This is in contrast with sediments below approximately 32 mbsf at Site GMMB01/02 outside Pockmark A, for which  Sultan et al. (2016)  considered lateral fluid advection due to the presence of distinct coarse-grained and, thus, highly permeable sediment layers and applied 2-D modeling. In this study, a 1-D numerical AOM-driven transport-reaction model was developed in gPROMS software (Process System Enterprise, PSE Ltd) to simulate the evolution of sulfate consumption by AOM with time: Φ ∂ C i ½ ∂t ¼ ∂ ∂x Φ D i ϑ 2 ∂ C i ½ ∂x ! -Φυ ∂ C i ½ ∂x -ΦR AOM ; ( 1 ) where t is the time (year), Φ is the sediment porosity, x is the depth within the sedimentary column (m), ν is the upward fluid velocity, ϑ is the sediment tortuosity, and C i and D i are the measured concentration (mM) and diffusion coefficient (m 2 year -1 ) of the dissolved species "i," respectively.
Pore Water Geochemistry Concentrations of pore water sulfate and chloride were investigated as indicators of upward methane flux intensities and markers of the evolution of the gas hydrate system. Chloride concentrations in all MeBo cores as well as sulfate concentrations in MeBo cores GMMB01/02 and GMMB05 (Figures  3b  and 3c ) were already reported in previous studies  (Sultan et al. , 2014 (Sultan et al. , , 2016;; Wei et al. , 2015). In gravity cores and pressure cores sea water-derived sulfate was found in sediments as deep as about 3. 2 mbsf (e. g. , GMGC17; Table  1 ). In general, shallowest positions of the SMI (0. 30-0. 65 mbsf) were found in the pockmark center (Figure  3b ; Table  S1 ), where seismic data and MeBo drilling indicated the shallowest ToGH (Figure  5 ). Deep pore water samples from core GMMB08 suggested that ascending fluids are generally sulfate-free.
2% pv (core GMDA01, "Rough Patch 1") is a minimum hydrate saturation. Nevertheless, the hydrate saturations obtained from pressure coring in this study substantiate that shallow hydrates in deepwater pockmarks have a significant methane storage capacity. In this context, it is worth mentioning that continuous intervals of deeper (greater than ~33 mbsf) massive gas hydrates several meters in lengths have been collected by drilling at the margins of a large pockmark located at the Umitaka Spur in the Japan Sea  (Snyder et al. , 2020). Order-of-magnitude estimates of dissolved methane concentrations in hydrate-free sediments below the sulfate zone can be obtained by considering data from pressure core GMDA05. Provided that the 4. 82 L of gas (99 vol. % CH 4 : 0. 21 mol CH 4 ) exclusively originated from pore water exsolution from the 119 cm-long interval (approximately 6.
18 4. 30/6. 95 Sultan et al. (2014); Wei et al. (2015) GMMB06 16021-1 1,148 Patch 1 6. 74 2. 50/3. 40 Sultan et al. (2014); Wei et al. (2015) GMMB07 16028-1 1,148 Patch 1 10. 19 2. 00/2. 85 Sultan et al. (2014); Wei et al. (2015) GMMB08 16030-1 1,142 Patch 1 56. 84 n. det. /5. 35 Sultan et al. (2014); Wei et al. (2015) GMMB10 16036-1 1,142 Patch 1 23. 95 1. 52/5. 15 Sultan et al. (2014); Wei et al. (2015); discharge of free gas during drilling GMMB11 16042-1 1,146 Patch 1 12. 57 n. det. /6. 65 Sultan et al. (2014); Wei et al. (2015); discharge of free gas during drilling GMMB12 16049-1 1,144 NE rim 24. 75 4. 20/7. 26 Sultan et al. (2014); Wei et al. (2015) Note. For sampling locations see Figure 1e. Additional specifics of MeBo cores can be found in the database of "Pangaea" ¼ PANGAEA -Data Publisher for Earth & Environmental Science (https://www.
Even though this study focused on the U. S. Pacific Northwest where the conditions are different to those of the European system, it is an indication that it is relevant to study these two effects together. In this study, we show how to integrate climate change effects into power system adequacy studies of continental scale. We focus on the impact of the change in temperatures on electricity demand in Europe, and the effect of the change in water inflow (seasonality and average flows) on hydroelectric generation. The overall idea of this study is to provide relevant insights into how to model climate change effects in large electricity market models without mobilizing full climate datasets, using a post-processing approach. The study analyses such effects for each of the market zones currently modelled in the European resource adequacy study (MAF -Mid-term adequacy forecast) by the European Network of Transmission System Operators of Electricity (ENTSO-E).
The IPCC (Intergovernmental Panel on Climate Change) Assessment Reports results are based on these models. Note that the form of the input data has evolved to integrate more parameters  [23] , such as the effect of emission policies and mitigation, considered in the different SRES  [24]  or RCP  [25]  trajectories. The output climate variables from GCM's can be used as inputs for other studies, in order to assess the effect of climate change on different systems. In the electricity system, it is expected that climate change impacts renewable energy sources (through changing wind speeds for wind plants, water flow patterns for hydro generation, or radiation and cloud patterns for solar plants)  [26] [27] [28]. Additionally, on the thermal power plant side, it is expected that climate change may reduce the available generation capacity through the change in cooling water temperature  [18]  as well as rising ambient temperatures  [29].
The European adequacy model An adequacy study is an evaluation of the security of supply in scenarios in which resources and demand are evolving. The goal of such studies is to support stakeholders in their decisions of investments and help identify risks, by focusing up to a few years ahead forecast. Here, we are using the base case reported in 2019 for the target year 2025 (in this study labelled ''MAF 2025''  [39] ) MAF studies combine deterministic scenario forecasts (generation, demand, planned generator outages) with random patterns (forced generator outages) and a historical, multi-temporal dataset that describes climate conditions and electricity demand  [40]. To do so, a random sample of certain years is created, in which the random patterns are combined with deterministic, historical climate patterns (past historical climate conditions including temperature, irradiation, wind speed, river water flows).
Methodology to modify river inflows For hydrological resource availability, the idea is to work in two steps: ''rescaling'' and ''reprofiling''. Rescaling is changing the total inflow in the year accordingly to the data found in literature as explained in II. C. Reprofiling is changing the seasonality of the input inflow following a trend found as found in literature, but without affecting the total inflow. A summary of the method is shown in Fig. 4. For ''reprofiling'', the following process has been pursued: -1st step: extracting profiles from literature Since we had no access to the output datasets of  [5] , on which we based our methodology, we needed to reconstruct the data by hand from the charts available in  [5]. To do so, we wrote a script doing the following: for a given set of coordinates of peaks (its value and the corresponding time), the scripts builds the profiles connecting the peaks one to another, in a weekly time series format.
Data used for demand The demand time series are forecasted by a software used by ENTSO-E which uses singular value decomposition to train and forecast load profiles models depending on various climate conditions  [42]. In addition, it identifies temperature-dependent and temperature-independent load shares and uses them to forecast adjustments, using scenarios of market evolution (penetration of heat pumps, of electric vehicles. ). This information about the evolution of the electricity market affecting demand is collected by ENTSO-E in what is called the PEMMDB (Pan European Market Modelling Data Base). The final demand file is an hourly forecast, for each market node and a given target year, of electricity load. To be more precise, the tool produces 35 different demand time series, corresponding to 35 years of historical climate data, on which there will be a Monte-Carlo simulation performed in the market modelling tool (see 3.
Data used for hydroelectric generation In our work, we manipulate the hydrological river run-off time series stored in PEMMDB. These files contain, for each market zone, historical data of 35 climate years of cumulated natural inflow into reservoirs and run-off river hydro generation. This data is collected centrally by ENTSO-E from all its European member TSO's as part of the PEMMDB. The hydrological data contains mainly weekly observations, except for the run-of-river where the temporal granularity of the information collected is daily data. Then, we used external data for the post-treatment of these hydro files to consider effects of climate change. Namely, for the hydrological resource availability, we took information from two studies: -For reprofiling the change in inflow profiles, we based our inputs on Forzieri et al.
2014  [5]. This study provides profiles of discharge in 11 different stations in Europe for a 'control period' ) and for projections (2071-2100). To modify MAF 2025, we used an intermediate value. Those projected profiles were calculated using data from climate simulations, based on the IPCC SRES (Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios) A1b. To reprofile the inflows of all the market nodes (most of which did not have a station and thus no data was available in the study), we used the data of the nearest station. -For rescaling the average change of total water inflow per country, we used the data found in the study by Van Vliet et al. 2013  [7]. We considered the percentage of change in hydro potential similar as the associated change of water inflow.
This may require moving towards modelling using probabilistic density functions with accurate representation of extreme events rather than creating a large set of equally weighted Monte-Carlo scenarios. It is without doubt that the chosen approach represents a simplification of all possible effects climate change could have on power system adequacy, including compound events or various likely climate change pathways. As the climate scenarios have similar effects of climate change in short run, this should not invalidate the results of the work presented. Additionally, the scenarios built in this study are consistent with the reference MAF 2019 study and build on a common dataset that includes all European TSOs. Specifically, by construction, the temperature sensitivity curves of demand have been preserved, and all the parameters of the MAF model are collected in a standardized process through the PEMMDB.
However, it should be mentioned that the coincidence of low hydro generation with peak temperature driven demand is not considered and may not reflect the adequacy situation in each European member state. In addition, the results here are indicative for an average year. Historic extreme events like the extreme cold spell from early 1985 are likely reasonably presented in this study but and any shift in frequency or magnitude of extreme events is not considered. To accurately assess extremes under climate change, an approach similar to K. van der Wiel  [45]  should be used. Future work should, when possible be based on a consistent set of assumptions to better capture possible linkages in climate change effects, i. e. following the method described by Zscheischler et al. 2018  [9] , in which the importance of a consistent dataset is highlighted by the correlation of the effects of climate change on intertwined elements of the electricity system.
Acknowledgements John Fazio ,  Massoud Jourabchi  and  Daniel Hua  are kindly acknowledged for their reviewing work and constant support. Vincent Deschamps  is also thanked for his review and advice. Ladislas Quazza  is kindly thanked for his reviewing work on the final version of the paper. The authors would like to thank  Nils Müller  for his help in the study of water inflows, use of market models and the  System Development section at ENTSO-E  for valuable feedback. L. P. Stoop received funding from the  Netherlands Organisation for Scientific Research (NWO)  under grant number  647. 003. 005.
As the detector, situ-ated a distance D from the sample, will in general not be mounted perfectly perpendicular to the incoming X-ray beam, an initial calibration of detector tilt and distance is needed. The detector tilt in relation to y l and z ! , as well as the wedge angle between z l and w ! , was calibrated following the procedure described in the documentation of the software package ImageD11  (Wright, 2005). For further discussion see e. g. Oddershede et al. (2010)  and  Borbely et al. (2014). The intersection between beam centre and detector forms the origin of the 2D Cartesian coordinate system y d -z d. The relation between a vector, v, in the laboratory coordinate system and in the sample system now becomes v l ¼ Xv ! ¼ cosð!Þ Àsinð!Þ 0 sinð!Þ cosð!Þ 0 0 0 1 2 4 3 5 v ! : ð1Þ Defining as the azimuthal angle measured from z d to a considered diffraction peak, the geometry of Fig.
To collect data from the entire volume of interest, the turntable, which holds the sample, is translated across the X-ray beam. This means that the rotation axis, z ! , is given a new position in relation to the laboratory coordinate system. At each position the sample is rotated continuously about the z ! axis and images are integrated and read out every d! degrees. The recorded 2D diffraction pattern at each y l , z l , ! setting is the integrated intensity measured over the step length, d!. In the following we refer to the collection of frames taken over a range of ! but at a single y l , z l setting as a 'framestack'. A point in the frame-stack is defined either by the three diffraction angles (, , !) of Fig. 1  or by use of the detector plane coordinates (y d , z d , !). The dimensionality of the complete data set is 5D (sample stage position y l , z l , ! and diffraction angles , ).