dataesr/scientific-paragraphs-categorization huggingface dataset:
Prior to the measurement, the powders were outgassed overnight at 120 • C and at least 7 h at 400 • C. The Specific Surface Areas (SSAs) were determined with both the Brunauer-Emmett-Teller (BET) and Langmuir models. The models were applied in pressure ranges suitable for obtaining a monolayer capacity included in the selected p/p 0 (for the BET) or p (for the Langmuir) linearization range  [26]. The cumulative pore volume and the pore size distribution were obtained by applying the Non-Local Density Functional Theory (NL-DFT); the pores were modelled as slit pores, using the "N 2 @ 77-Carbon, original DFT" available in the Microactive software  [27]. The model was applied in the whole range of p/p 0 with a regularization of 10 -3. The infrared (IR) spectra of the calcined samples were recorded both in transmission and in Attenuated Total Reflectance (ATR) mode.
One Pulse (OP) 29 Si and Cross Polarization (CP) 29 Si{ 1 H} Magic-Angle Spinning (MAS) Nuclear Magnetic Resonance (NMR) spectra were acquired at 79. 4 MHz on a Bruker Avance III-HD 400 (9. 4 T), using 4. 0-mm outer diameter probe. For OP, a radiofrequency of 30 kHz and a recycle delay of 15 s was used. A recycle delay of 5 s and a contact time of 5 ms were used for 29 Si CP-MAS experiments. For 29 Si OP-and CP-MAS experiments 4096 and 2048 transients were acquired respectively. The 29 Si OP-MAS spectra of the calcined rose-ITQ-1-calcA and rose-ITQ-1-calcB and of the as-synthesized rose-ITQ-1-HS samples were deconvoluted with Lorentzian peaks, using the program Fityk. For the calcined samples, the deconvoluted peaks were added at chemical shift values that were kept coherent with theoretical chemical shifts computed with the following equation (eq.
(  1 ))  [28]. Where δ iso is the chemical shift (ppm), 〈r SiO 〉 is the average Si-O distance, σ(r SiO ) is its standard deviation, 〈θ SiOSi 〉 is the average Si-O-Si angle and σ(θ SiOSi ) is its standard deviation. The Si-O-Si angles and the Si-O distances were obtained from the cif file for the MWW framework, downloaded from the Database of Zeolite Structures present on the website of the International Zeolite Association (IZA)  [29] , without further refinement. 13 C OP-MAS-NMR spectra of the as-synthesized rose-ITQ-1-HS and sf-ITQ-1-HS samples were acquired at 100. 6 MHz on a Bruker Avance III-HD 400 (9. 4 T), using 4. 0-mm outer diameter probe. Radiofrequency pulses of 38. 5 kHz and a recycle delay of 2 s were used. For each spectrum, 6000 transients were acquired. The elemental analysis of the as-synthesized samples was performed using a Thermo Nicolet FlashEA 1112 Series CHNS analyzer; the measurement was repeated three times for each as-synthesized sample and the results were averaged.
Such a big change in the 29 Si OP-MAS-NMR spectra before and after calcination suggests an important structural change, that occurs jointly with the layer condensation. The 29 Si OP-MAS-NMR spectral components of the calcined rose-ITQ-1-calcA and rose-ITQ-1-calcB samples were deconvoluted keeping the number of peaks coherent with the number of crystallographic sites of the MWW framework (eight crystallographic sites, named as reported in the Database of Zeolites Structure in the IZA website)  [5]. The number of peaks used to deconvolute the spectrum of rose-ITQ-1-HS was considered the same as in the calcined samples, assuming that no modification of the hexagonal symmetry system occurs upon calcination, save the contraction of the c cell parameter  [14, 45]. This is a rough approximation, since the Si-OH groups at the surface of the single layers in rose-ITQ-1-HS, that may contribute to the signal at -94 ppm, are in principle 4 over 72 Si atoms in a unit cell (≈5 %).
Acknowledgements ED and SM acknowledge the support of the  Centre for Zeolites and Nanoporous Materials, Label of Excellence, Normandy Region (CLEAR). FR, AA, MS, SB, VC and FB acknowledge support from the Project CH4. 0 under the  MUR program "Dipartimenti di Eccellenza 2023 -2027 " (CUP:  D13C22003520001 ).
Regional atmospheric model Our goal is to disentangle the contribution of large-scale and boundary-layer drivers in shaping the near-surface winds of Antarctica. In order to do this, we need a description of the vertical atmospheric column, which is only available in radiosoundings at the two extremities of our transect, DDU and DC. Consequently, due to the scarcity of observations, we perform our study using outputs from the regional atmospheric model MAR v3. 11 on the period 2010-2020 (https://gitlab. com/Mar-Group/MARv3), after evaluation of this model for near-surface winds (Section 3. 1). MAR is a regional hydrostatic model that takes into account specific physical properties of the Antarctic region, in particular a multi-layer snow model based on CROCUS  (Brun et al. , 1992; Vionnet et al.
S6. The MBD captures well the temporal variations and extrema of the pressure gradient force (Fig. 5 ). Some of the maxima are underestimated (at D47, our MBD exhibits a mean bias of -1. 3 m s -1 h -1 ). This is due to the fact that the background potential temperature profile (θ 0 ) is approximated by a linear slope, which is not always exactly the case, and causes an under-estimation of the large-scale acceleration, particularly near the coast (D17), where the vertical structure of air masses is more complex. Quantitatively, the normalized root mean square error (NRMSE, i. e. the root mean square error between MAR PGF and our  was about 7. 5 % for July 2010-2020 at the surface. The coefficient of determination between the July datasets is relatively high everywhere on the transect (R 2 > 0.
230 MAR MBD is performed in August 2012, for two successive high wind speed events (HWSE). HWSE are defined as days for which the total wind speed is greater than the 90 th percentile of the 10 years 3-hourly dataset. During the first event on August 7 th , the katabatic layer (the air mass cooled down by the surface) starts growing around 00:30 (UTC), reaches its maximum around 19:30 (UTC) (Fig. 6a ) and decreases during the next 24 hours. This is accompanied by an increase of background temperature (up to 288 K, Fig. 6b ), which, combined with the low potential temperature, creates a strong potential 235 temperature inversion (∆ θ = -22. 2 K, Fig. 6c ) and vertically integrated potential temperature deficit(Fig. 6d ). This katabatic layer development is characteristic of a katabatic event  (Vihma et al.
Drivers of seasonal wind variability on the transect The wind speed displays a seasonal cycle that peaks in late winter (August to September) and is especially pronounced in the low elevation and coastal areas. In Fig. 8 , we compute the annual cycle of the total wind speed (average of 3-hourly time steps for 2010-2020), and of wind speed equivalent to large-scale acceleration (V LSC ), thermal wind (V THWD ), katabatic (V KAT ) and turbulent accelerations (V TURB ). Below 1500 m asl, the seasonal amplitude in wind speed between summer and winter (∆|WS| JJA-DJF equals 5. 6 m s -1 at D17 and 3. 8 m s -1 at D47) is larger than the July standard deviation of 3-hourly July wind speed (highest variability during winter months) computed over the 10 years dataset: (σ |WS| equals 4. 1 m s -1 at D17 and 3.
Overall, on our transect, there is a satisfactory low number of profiles exhibiting large NRMSE. Increasing the temporal resolution of our dataset would be even more challenging. The vertical profiles of potential temperature would be even less smooth and hard to interpolate. Furthermore, the stationary approximation has been made at a 3-hourly time-scale, which is generally valid (Section 4. 1), but would not be accurate at a finer resolution. Finally, it is crucial to have a good depiction of the vertical structure of the atmosphere, inside and above boundary layer, to study the drivers of near-surface winds. Our regional atmospheric model has been evaluated at 2 m agl and performs well at that height. However, its ability to accurately represent vertical atmospheric profiles has not been assessed due to limited observations, only available at DC where there is no katabatic acceleration, and DDU where the performance of MAR is limited.
One simulation was forced by unmodified atmosphericforcing data, while the other was forced by a filtered atmospheric dataset with the signal of the mistral removed from the forcing. Thus, the ocean response due to the mistral events could be separated and examined, revealing the effects of seasonal atmospheric change alone. A multitude of observational data were collected during this year in the framework of the HYdrological cycle in the Mediterranean EXperiment (HyMeX)  (Estournel et al. , 2016b; Drobinski et al. , 2014) , which provided a solid base of observations to validate the ocean model results. In particular, our findings quantify the following items: the separated and combined effect of the mistral and seasonal atmospheric cycle on deep convection, the dominant attribute of the mistral causing buoyancy loss, the source of the buoyancy loss due to the seasonal atmospheric cycle.
2 ) over the entire domain shown in Fig. 1a. These variables were chosen as they are the primary variables that affect the surface flux calculations in the bulk formulae (Eqs. 1-4). The variables relating to radiation and precipitation fluxes were left unchanged. The filtering removes the short-term anomaly-scale forcing from the forcing dataset (the phenomena with timescales of under a month), effectively removing the mistral's influence on the ocean response. This creates two separate forcing datasets, one with the anomaly-scale forcing included and one with just the seasonal-scale forcing (hence the designation of control and seasonal). The filtering process was performed by a moving window average: χ i = 1 i + N + 1 i+N j =0 x j , (6) where χ i is the averaged (filtered) value at index i of a time series of variable x with length n, where i = 0 → n.
The window size is equal to 2N +1, which in this case is equal to 31 d. The ends have a reduced window size for averaging and thus show edge effects. The edge effects did not affect the forcing used for the NEMO simulations, as they were before and after the ocean simulation dates, as two full-year atmosphericforcing datasets were used for the simulations. The moving window average was applied to each time point per day over a 31 d window (i. e. for 3-hourly data, the time series is split into eight separate series, one for each timestamp per day -00:00, 03:00, 06:00, etc. -then averaged with a moving window before being recombined). This was done to retain the average intra-day variability yet smooth the intra-monthly patterns, as the diurnal cycle has been shown to retard destratification by temporarily reforming a stratified layer at the sea surface during slight daytime warming.
Mistral events Mistral events will be used for developing the simple model in Sect. 4 for their role in driving buoyancy loss at the ocean surface. Events were determined from the WRF/ORCHIDEE dataset in combination with the ERA-Interim Reanalysis dataset  (Dee et al. , 2011). Two main criteria were used to define a Mistral event: 1. northerly flow with a streamwise flow direction ±45 • about the south cardinal direction above 2 m s -1 at two locations simultaneously, i. e. , at Montélimar, France (45. 5569 • N, 4. 7495 • E), and in the GOL (42. 6662 • N, 4. 4372 • E), 2. the presence of a Genoa Low, defined as a closed sea level pressure contour around a minimum in the field, using 0. 5 hPa intervals, anywhere in the box defined by the latitudes 38 and 44 • N and longitudes 4 and 14 • E (a slightly different domain than that of  Givon et al.
If N 2 is assumed to be constant throughout the water column, the integral simplifies to SI = D 2 2 N 2. ( 9 ) SI provides a zero-dimensional index to track stratification and can be easily related to the buoyancy loss experienced by the water column due to the atmosphere. Because of this, in this article SI will be used as the diagnostic to track the atmosphere's impact on the stratification of the GOL waters. 6 ). Here the variables q, T , u, and v are shown for both the unfiltered (control, black) and filtered (seasonal, blue) datasets at the nearest grid point to 42 • N, 5 • E. Note how the peaks of the time series are removed and the general wind direction is conserved. where the data are available, NEMO corresponds well with the observations. The same is true for the Lion buoy data, which had measurements for the entire time covered by the simulations.
Model validation Additionally, the control simulation density and potential temperature profiles were compared to conductivitytemperature-depth (CTD) measurements also procured from the HyMeX database. The CTD measurements were collected during the HyMeX Special Observation Period 2  (Taupier-Letage, 2013; Estournel et al. , 2016a; Drobinski et al. , 2014)  mission. The CTD profiles collected at approximately the same time and location were averaged together to adjust for small variances and gaps in the data. The averaged profiles and their standard deviations are visualized in Figs. 5  and 6. The locations of the CTD profiles are shown in Fig. 4. Like with the SST comparisons, the profiles from the nearest grid point in the control simulation domain were used for the CTD comparisons. The root-mean-square error (RMSE) and bias (calculated as the difference between the model values and the observation values) for each of the averaged CTD profiles and corresponding control simulation profiles was calculated and is presented in Table  2.
Overall, the control simulation and CTD profiles are decently well correlated but not perfect, with low RMSE and bias for both density and potential temperature. The density profiles have an average RMSE that is lower than the average RMSE for the potential temperature profiles: 0. 025 kg m -3 and 0. 094 • C, respectively. Argo float profiles from the HyMeX database were also compared to the control simulation, again with profiles from the nearest grid point being used. A total of 3118 potential temperature profiles within the box, bounded by the 40 to 44 • N latitudes and the 2 to 8 • longitudes, representing the GOL area were considered (see Fig. 4 ). The average RMSE between the Argo profiles and control simulation profiles was 0. 43 • C, with an average bias of 0. 23 • C. These values are larger than the values of the comparison with the CTD profiles.
Acknowledgements. This work is a contribution to the  HyMeX program  (HYdrological cycle in the Mediterranean EXperiment) through  INSU-MISTRALS support and the Med-CORDEX program  ( COordinated Regional climate Downscaling EXperiment -Mediterranean region ). It was also supported by a joint  CNRS-Weizmann Institute of Science collaborative project. The authors acknowledge  Météo-France  for supplying the data and the HyMeX database teams (ESPRI/IPSL and  SEDOO/Observatoire Midi-Pyrénées)  for their help in accessing the data. Financial support. This research has been supported by the  Centre National de la Recherche Scientifique (CNRS)  in the framework of a collaborative project shared between the CNRS and the  Weizmann Institute of Science. Review statement. This paper was edited by  Markus Meier  and reviewed by two anonymous referees.
Code availability. The RegIPSL model and the NEMO model and its configuration used in this paper can be accessed at the following Git repository: https://gitlab. in2p3. fr/ipsl/lmd/intro/regipsl/ regipsl (Drobinski et al. , 2012). Code used to generate figures and implement the simple model can be found at the following Git repository: https://gitlab. com/dkllrjr/untangling_deep_conv_2012_ 2013_code  (Keller, 2022). Data availability. The ERA-Interim Reanalysis can be found at https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era-interim (Dee et al. , 2011). HyMeX data are available at https: //mistrals. sedoo. fr/HyMeX/ with registration (last access: 24 March 2022) (Drobinski et al. , 2014; Taupier-Letage, 2013). The RegIPSL and NEMO dataset can be made available by the corresponding author upon request.
Household sampling and survey Data were collected during household surveys conducted from June to August 2013 and concerned the 2012 cropping season (from June 2012 to May 2013). The exhaustive list of the households in each village and the population data were extracted from the Niakhar observatory data base  (Delaunay et al. , 2013). The surveys in Barry were exhaustive (73 households). In Diohine, a sample of 44 households was surveyed, corresponding to the district named Sassem. In Diohine, which is a large village comprising a total of 354 households, the districts are regulated by notables who are responsible for the management of the natural resources (e. g. the common fallow). Sassem district is representative of Diohine's agricultural activities, practices and access to resources.
Computing biomass flows Biomass flows can be human driven (all biomass handled by farmers and used at the homestead, e. g. food and wood; supplied to the herd, e. g. feed concentrate and fodder; or used on the plots, e. g. fertilizers, manure and seeds) or herd driven (ingestion during grazing and excretion during grazing and night corralling). Human driven flows were quantified directly with the farmers during the surveys using local units of volume or weight (cart, bundle, etc. ). The flows were then converted into standard units (kgDM). Conversion factors were defined by weightings made during the study or based on data in the literature  (Vandermeersch et al. , 2013, Appendix 1). Herd-driven flows were derived from information concerning herd feeding practices and the time spent between the four land units within the boundaries of the village terroir (dwelling, home fields, bush fields, rangelands) collected during the surveys.
Herd-driven flows were calculated (in kgDM season -1 ) for each practical season (wet season, cool dry season, hot dry season) defined in section 2. 1 above. Free-grazing animals ingest the biomass available in plots: crop residues in cultivated fields after harvest and grass growing on common fallow land and rangelands all year round. Part of the biomass is returned to the soil via animal excreta. Both animal ingestion (i. e. feed intake) and excreta (i. e. dung and urine) flows were calculated based on coefficients derived from field observations of local herds  (Wade et al. , 2017)  or from data on West Africa available in the literature (Appendixes 2 and 3). Free grazing results in flows of biomass between households that depend on the balance between the forage biomass available on the plots belonging to the household and the need for forage by herds belonging to the same household.
Acknowledgements The authors thank the 117 surveyed farmers for their availability and their interest in the study. They also thank  Ibrahima Thiaw  and  Mamadou Lamine Ndiaye , cartography students at  Dakar Cheikh Anta Diop University  ( UCAD ), who performed parts of plot georeferencing and produced the maps used in this article. This research was supported by the  European Union  [grant number  FP7-KBBE-2010   AnimalChange  project]; and the  French national  research agency [grant number  ANR-13-Agro-002 Cerao  project].
We gathered data from all colonies in the same dataset and included the colonies as a group effect in the models to provide nest abundance estimates for each colony and test potential detection probability differences between them. We analysed data from the reproduction peak sessions (see 'Results') for year1 and year2 separately. One session data series corresponded to the six daily yovers (year1), and the other to the one daily yover on three successive days (year2). We hypothesized that the 'nest population' was closed (no appearance or disappearance of nests) within these closed sessions. We considered the active nests as those occupied by a pond heron during at least one yover. The nest detection history for a yover was 1 when an occupied nest was detected by the image interpreter and 0 when it was not detected.
Capture-recapture dataset simulations to perform power tests The mean coe cient of variation (cv) in abundance obtained from simulations was high for two yovers, especially for small colonies (50 individuals) (cv = 0. 13) (Fig. 6 ). Such a high cv did not allow the detection of a decline in abundance lower than 16% (see Table  S2  in Supplementary Materials). This cv decreased sharply for three yovers, with cv = 0. 05 for colonies of less than 50 individuals and cv < 0. 05 for colonies of more than 50 individuals. These lower cvs allow the detection of a decline in abundance as low as 5-6% depending on the colony size. When considering four yovers, cv < 0. 03 whatever the size of the colony, allowing the detection of a decline in abundance of 2-3%.
These process steps are widely accepted and used in literature reviews in SCM  [29]  and their validity and reliability is made sure by following state-of-the-art guidelines for designing such reviews  [18]  and avoiding potential biases  [36]. Step 1. -material collectionconsists of research question formulation, search parameters definition, database selection, and material extraction. While the research question formulation can be found in the introduction section, the search string definition is based on identifying the most appropriate keywords sets, i. e. , energy production keywords, sustainability-related keywords, and SCM-related keywords, which are combined with the Boolean operator "AND". The Scopus database was chosen, as we see it as the first and the most accurate database that can provide high-quality results  [30, 31] , which in the end led us to the material extraction.
Finally, to make sure that the study is comprehensive, a snowballing approach was conducted, by checking references cited by the included papers in the first result, as well as by including relevant articles (with managerial focus) highlighted by Google Scholar and Scopus databases. As a result, a sample of ninety-seven papers was included for further analysis (See Appendix A for further detail). Step 2. -descriptive analysesencompassed the analysis of the distribution of the papers by energy source, applied research methodology, journal outlet, year of publication, and geographical focus of the study object. The related results are presented in section 3. 1. Step 3. -category selectionfollowed the default two-step approach proposed by Seuring and Gold  [24]. Categories were defined a priori drawing from Seuring and Müller  [15] , Ahi and Searcy  [14] , Brandenburg et al.
[37]. Step 4. -material extractionfocused on topics, ideas, and patterns of meaning that come up repeatedly in the literature. This was done by analyzing the categories of key sustainability characteristics and key SCM characteristics as presented in Table  1. The process was done by coding the papers according to the categories defined in step 3 using the MAXQDA software to support the documentation of the manually conducted coding, which led to analyzing and drawing a conclusion according to the coding results. The coding and data analysis procedure gave results in two ways. First, the frequency distribution among the categories is discussed, to know the prevailing categories in the literature. Second, the individual papers' specific topics, which could provide underlying discussions in the field.