dataesr/scientific-paragraphs-categorization huggingface dataset:
These efforts, moreover, show a more balanced consideration of environmental and social sustainability challenges  [76, 78]  that are connected to energy production through the SC and especially the material flow. In effect, an extension of these upstream sustainability efforts towards energy production could help to strengthen the combined consideration of material, information, and capital flow. The limitations of this study are divided into two main categories: data collection and data analysis limitations. The data collection is limited to the use of the Scopus database and the applied keywords, which may limit the collection of the related articles. However, Scopus is one of the most reliable databases for research purposes  [30, 31]  and the keywords have been derived from theory and use a wide range of synonyms to cover the fields of interest in a comprehensive way  [36].
In contrast, at 58°S deep enhancement of Chl and C did not exceed ~25% in any profile. If the emerging DCM/DBM observed at the 58°S is to be interpreted as a precursor to the more established feature observed as 56°S then this suggests that subsurface biomass accumulation already exists below the mixed layer at 58°S (even more so than at 56°S) but does not emerge as a maximum relative to the surfacer layer until overlaying phytoplankton stocks are grazed to levels lower than in the subsurface feature. The seasonality and downward export potential of the DCM observed in the IPFZ at the 56°S site were explored using the BGC-ARGO float. The float datasets extended the week-long shipboard observations for 3-4 months within the IPFZ water mass (S-Figure  2a ) and thus can be cautiously linked to shipboard trends presented in Figures  1,2  and 5.
Ecological and biogeochemical roles of the DCM/DBM Recent reviews of S. Ocean NPP  (Pinkerton et al. , 2021)  and biogeochemistry  (Henley et al. , 2021)  suggest DCMs play important ecological and/or biogeochemical roles based on particle export. Our suite of datasets from diverse observational platforms offers insights into the wider roles of DCMs/DBMs. The co-location of both the DCM and DBM, which is mainly associated with diatoms, along with its multi-month persistence suggests that it is a subsurface niche that develops as a result of the decline of the surface mixed layer phytoplankton community (S-Figure  4  and S-Table  1 ) following resource limitation by both silicate and Fe. The surface waters appear, based on the BGC-ARGO float records (Figure  9 ), to have low and constant Chl and C concentrations, that characterise a seasonal High-Nutrient Low-Chlorophyll province  (Boyd et al.
d) Climate data Species living in harsh conditions may be constrained by a shorter active season that might influence sex differences in hibernation phenology. To take this into account in the models (see section "Statistics"), the location (latitude and longitude) of hibernation study sites were recorded, and when not provided we determined their location using Google Earth and the location description. Then the location data were used to extract values of the mean temperature of the coldest month (known as "BIO6" in the database and hereafter referred to as minimum temperature) from an interpolated climate surface (BIOCLIM) with 1 km 2 resolution (30 sec) based on data for the period 1970-2000  (Hijmans et al. 2005 ).
These parameters may also soften the effects of reproductive parameters on hibernation phenology. Thus, we tested for a "two factor interaction" between either temperature or delay in mating or food-storing with change in body mass before mating or with relative testes mass. Table  1  describes the models tested, with their respective sample sizes (see supplementary materials S2 for datasets). In the case of multi-factor models, we used the dredge function of the MuMIn package (version 1. 43. 17; Barto 2020) to select the best model based on the corrected Akaike information criterion (AICc). Normality and homoscedasticity were checked by graphical observation. We tested for multicollinearity using the variance inflation factor (we required VIF < 3) on linear models including the factors of the best models.
Data from the years 2018 to 2022, specifically from the Aulne River in Brittany, were collected from the reference HydroPortail database version 3. 1. 4. 3 (HydroPortail, 2023). Two constraints were considered when calculating the water flows: the inflow into the fish farm could not exceed 1. 5 m 3 s -1 , and a maximum of 90% of the total river flow could be derived to the fish farm. To predict the daily water inflows into the fish farm, a Generalized Additive Model (GAM) was then employed considering the different constraints (Figure  3C ).
Life cycle inventory The life cycle inventory (LCI), presented in Table  3 , was conducted by running our farm model with the specifications for both conventional and organic production scenarios. All the inputs and outputs were calculated using all the results from each batch of fish over one year of routine production generated as described in the farm model. The Agribalyse version 3. 0  (Koch and Salou, 2022)  and Ecoinvent version 3. 8  (Wernet et al. , 2016)  databases were used to obtain the necessary data for conducting the assessment. Both databases are grounded on the recommendations in international standards  (Wolf et al. , 2012). (1) Production of purchased feed -Crop-derived ingredients used in fish feed mainly originated from Brazil and France (e. g. soybean meal from Brazil and wheat bran from France).
Fishderived ingredients originated from the Peruvian and the Norwegian fish milling industry (e. g. fish meal from Peru and fish meal from fish trimming from Norway). The exact composition of the different feeds used and their nutritional values were given by the feed manufacturer (Le Gouessant, personal communication). The transport of feed ingredients to feed manufacturers in France was by trans-oceanic ship and by lorry (>32 t), whereas the transport of feed from France to the fish farm in Brittany was by lorry (>32 t). Road distances were calculated from Google Maps and ocean distances were assessed from shiptraffic. net. Other data required to compute the environmental impact of feed ingredients were based on the literature  (Boissy et al. , 2011; Pelletier et al. , 2009). (2) Production of energy expended on the farm -The electricity used by the farm was coming from the French energy mix in the Ecoinvent database.
To achieve this, we have used not only the ReCiPe method but also CML baseline method  (Guinée, 2002). The latter was commonly used in previous LCA studies focusing on rainbow trout aquaculture while ReCiPe was only recently used in a rainbow trout aquaculture context notably in Italy  (Maiolo et al. , 2021)  and Spain  (Sanchez-Matos et al. , 2023). Overall, the literature comparison corroborated our findings when expressed environment impact per tonne of trout (Table  6 ). Indeed, our results are consistent with the literature  (Aubin et al. , 2009; Boissy et al. , 2011; Chen et al. , 2015; d'Orbcastel et al. , 2009; Dekamin et al. , 2015; Maiolo et al. , 2021; Papatryphon et al. , 2004; Samuel-Fitwi et al. , 2013)  even if the ranges of reported values can be wide. Despite uncertainties related to varying inventory databases and CML assessment method versions, another underlying cause of the differences in environmental impacts among studies is the use of diverse production systems and varying FCRs to achieve producing the same quantity of trout  (Philis et al.
Photosynthesis and respiration rate At the end of the experiment, rates of net photosynthesis (P n ), gross photosynthesis (P g ), and respiration (R) were estimated from the increase (for P n and P g ) or decrease (for R) in the O 2 concentration in seawater, where P g is the sum of the absolute values of P n and R. Coral fragments (n = 6 per condition, 3 per tank) were individually put in Plexiglass® chambers (60 mL) filled with 0. 45-μm-filtered seawater. The incubation medium was kept at 25 • C and constantly agitated using a magnetic stirrer. The oxygen concentration was measured with an oxygen sensor (polymer optical fiber, PreSens®) connected to an Oxy-4 (channel fiber-optic oxygen meter, PreSens®) and was recorded with the Oxy4v2-30fb software. P n was measured at 200 μmol photons m -2 s -1 irradiance for 45 min while R was measured in the dark.
Values of light intensities received by the corals during RLCs were obtained using the internal Light-Calibration program of the Diving-PAM. The amount of energy dissipated as heat (NPQ) is defined by  Bilger and Björkman (1990)  as NPQ = F m F ′ m - 1 After the RLC treatment, coral nubbins remained in darkness to assess the NPQ recovery after 1, 3, and 10 min. The recorded F v /F m , Φ PSII , rETR, and NPQ were plotted as a function of PAR. The data is visualized by using the WinControl_v. 3. 29 program (Walz GmbH®, Effeltrich, Germany).
Acknowledgments The authors are grateful for the laboratory assistance from  Maria-Isabelle Marcus ,  Romie Tignat-Perrier , and  Alice Blanckaert  at the  Centre Scientifique de Monaco (Monaco ). Eric Béraud  ( Centre Scientifique de Monaco ) helped with the interpretation of the chlorophyll fluorescence data. We acknowledge  John B. Mwansa  ( Barbados Water Authority ) for collecting and providing the volcanic ash samples. Last but not least, the authors would like to thank the reviewers for expanding the scope of this paper and helped us improving the quality of the manuscript. This work was funded by the  Swiss National Science Foundation  (project  PCEFP2_194204 ).
Analysis Analyzing metrics derived from diverse models, executed on multiple platforms, and managed by independent institutions presents a non-trivial challenge. Moreover, the presence of missing values further complicates the analysis, making it difficult to substitute them with estimations or interpolations, particularly given the relatively limited size of the dataset. Our approach consisted of first: validating the metrics provided by the institutions. We have sometimes found that the metrics reported for some models were orders of magnitude apart from the rest. In this case, we started actively communicating with the institutions asking them to double-check the values and assisting them in the re-computation process. After going through this process for each one of the metrics and models we came up with the values reported in Sec.
Carbon footprint In addition to the CPMIP collection, we have also gathered the general metrics shown in Tab. 10. These metrics provide both useful (only accounting for simulations that produced data with scientific value) and total (encompassing all simulations, including spin-up and any runs that were finally discarded) numbers for the complete execution of CMIP6 experiments at the different institutions. They can be used to provide an idea about the total and useful number of years simulated, data produced and core hours consumed to finish the European community CMIP6 experiments. Although we did our best to collect the most updated data, we are aware that these numbers could have changed since the data collection was finished. We know that some institutions were doing some minor and final executions and updating databases such as ESGF.
These collaborations underscore the shared concern of multiple institutions regarding the computational performance in climate science and the joint effort to estimate the carbon footprint of the simulations conducted during the CMIP6 exercise. Finally, the paper addresses the main issues and drawbacks encountered during the collection and analysis of the metrics. These points should be of particular interest to the partners, aiming to improve and facilitate future collections. The paper also proposes recommendations to confront these challenges, which can be adopted by the community for the development of novel tools and more finely-grained metrics which would facilitate upcoming similar works. Continuous collection of these metrics in future multi-model projects will facilitate the development of a shared database for the community and vendors.
Acknowledgements. The research leading to these results has received funding from the  EU   H2020 ISENES3 , under grant agreement n°310  824084  and co-funding from the  National Research Agency  through  OEMES  ( PID2020-116324RA-I00 ) https://doi. org/10. 5194/gmd-2023-188  Preprint. Discussion started: 23 October 2023 c Author(s) 2023. CC BY 4. 0  License.
Description of the data, system boundaries, and experimental management The boundary for this study was a 4-year cycle (Fig. 1 ) that contained four annual cycles of beef cattle [from yearling (~340 kg BW) to slaughter (~490 kg BW)]. The GHG emissions were calculated using IPCC methodology (IPCC, 2019a) and country-specific equations, combined with the dataset for the large-scale integrated-system experiments developed at the Embrapa Agrossilvipastoril, Sinop, Mato Grosso, Brazil. The experiment was performed in a randomized complete block design with four systems (treatments) and four replicates (blocks), for a total of 16 experimental units. Each plot had an area of 2 ha (200 m eastwest × 100 m north-south), for a total of 32 ha of experimental area. The systems evaluated were livestock (L), livestock-forestry (LF), crop-livestock (CL), and crop-livestock-forestry (CLF) (Fig.
1 % of body weight (BW) in the first year and 0. 2 % of BW, the second year on. The animal production dataset was generated from June 2015 to May 2018, including both dry and rainy seasons. The animals were weighed every 28 days after 16-h fasting (feed and water). The stocking rate was determined by dividing the average weight of the animals (testers and grazers) by the number of days in the experimental units  (Petersen and Lucas, 1968). For the average daily gain (ADG) calculation, only testers were used. Values for the fourth year of the livestock component were estimated as means of the three previous years. Forage mass was quantified every 28 days. In the L and CL systems, four exclosure cages (0. 64 m 2 and 1. 2 m tall) were strategically positioned within each experimental unit. In the LF and CLF systems, eight exclosure cages were placed, four on the north and four on the south face of the central rows of trees.
Statistical analyses After verifying homogeneity of variance for each linear predictor, quantil-quantile plots of the residuals, and the normality of residues using the Shapiro-Wilk test, data were analyzed using the mixed-model method (PROC MIXED) with parametric structure in the covariance matrix of SAS® software, version 9. 4 (SAS, 2014). For the enteric CH 4 variables, block and year were considered as random effects, and the system as a fixed effect in the following model: Y ijk = μ + S i + B j + e ij + Y k + ε jk where Y ijk = the ijk dependent variable, μ = overall mean, S i = fixed effect of the i th system, B j = random effect of the j th block, e ij = error associated with system i in block j ~ normally and independently distributed, Y k = random effect of year, and ε jk = experimental error ~ normally distributed.
02. 13. 11. 001. 00. 00), Acrimat (Association of Mato Grosso farmers), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil; 479409/2013-7 and 303438/2015-0) and the ICLF Network. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001. The authors acknowledge the Sao Paulo Research Foundation (FAPESP) (grant no. 2019/26609-8 and 2021/14691-1) for granting a scholarship to A. M. , and financial support through the partners of the Joint Call of the Cofund ERA-NET SusCrop (grant no. 771134), FACCE ERA-GAS (grant no. 696356), ICT-AGRI-FOOD (grant no. 862665), and SusAn (grant no. 696231), the French Agence Nationale de la Recherche (project ANR-21-SUGA-0001-04) and Acrinorte (Association of Northern Mato Grosso farmers; 44.
Somalia had experienced extreme flash floods in the recent years across the arid regions causing tremendous loss in lives and properties. However, the flood magnitude, depth, and frequency of occurrence is not yet quantified due scarce datasets. In this study, integration of observed and Climate Hazards Infrared Precipitation (CHIRP) satellite rainfall products along with various remote sensing raster data were used to improve hydrological model simulation outputs. The Hydrologic Engineering Center namely HEC-HMS and HEC-RAS models were used to simulate the rainfall-runoff processes and flood inundation, respectively using land use, soil, slope and Digital Elevation map (DEM) datasets. The HEC-HMS model calibration and validation of simulated flows generated by the model predicted good agreement with the observed flow with values of 0.
2017). We also note that remote sensing and GIS, along with other fuzzy theories and statistical analysis of hydrological peak discharge and GIS environment, can result in accurate flood hazard mapping, estimating the risk of every land use/land cover  (Mousavi et al. 2019). Other scholar uses multi-criteria decision analysis with a combination of GIS  (Rincón et al. 2018). Recent studies applied different hydrodynamic models to estimate flood depth and inundation mapping. In many studies area, hydraulic/hydrodynamic simulation through the coupled 1D/2D model suing HEC-RAS from observed river discharge and DEM datasets. Manning's roughness coefficient was used as the basic parameters in the estimation of floodplain extent  (Sarchani et al. 2020). However, only a few researchers have already exploited this tool to investigate the efficiency in producing reliable flood mapping using different datasets especially in data scarce regions.
We note that, before the civil war, there was a structural mitigation of flood gate. After civil war, since the structure collapsed, then the floods become a common problem in the area. Hence, flood mitigation mechanisms are important to secure human and their properties. Therefore, this study aims to J o u r n a l P r e -p r o o f analyze flood magnitude and inundation mapping of Beledwayne town the Wabi Shebelle river basin in Somalia. To achieve the main aim of this study, the following main objectives were drawn: (i) estimate flood frequency analysis using best distributions functions for the flood event (ii) analyze flood mapping using hydrology and hydraulic model performances (iii) estimate how flood mitigation measures reduce the floodplain extent in the study area. This study is unique as due to its integration of climate and satellite rainfall datasets coupling with hydrological and hydraulic model for flood inundation mapping plus introduces possible mitigation options.
Meteorological data In this study, observed and satellite meteorological datasets were used. The climate dataset covers the period from 2005 to 2018 at daily time scales. These datasets include precipitation, J o u r n a l P r e -p r o o f maximum and minimum temperature, wind speed, relative humidity, and sunshine duration. The climate data were obtained from the National Meteorological Agency of Somalia stations of Beledwayne, located near the study area. The rainfall data is obtained from Climate Hazards Group InfraRed Precipitation (CHIRP) satellite-only product, at (daily temporal and 5. 5km spatial resolution) extracted from http://chg. geog. ucsb. edu/data/. CHIRP covers regions that are situated between 50 o S-50 o N latitude and all longitudes  (Goshime et al. 2019; Funk et al. 2015).
Hydrological data All of the hydrometric stations operated by the Ministry of Water and Energy of Somalia. In 2002, Somalia water land and information management  (Ec et al. 2008 ) began rehabilitating the hydrometric network with restored and fitted the staff gauges from March 22 to April 4, 2002. The new staff gauges were installed, and the river Shabelle gauge station was maintained before the war by the Ministry of Agriculture's hydrometric project. To date, two stations have been reinstated measuring the river flow at Shabelle  (Hussein 2015). Flow data was collected SWALIM and which is an important aspect of modeling a water system and helps in understanding how it operates under a variety of hydrologic conditions. For this study, the.
Spatial Data The digital elevation map (DEM) of the study area were extracted from high-resolution (12. 5 × 12. 5 m) ALASKA (www. https://asf. alaska. edu) for processing catchment delineation, river cross-section and prepare HEC-RAS input data to develop flood inundation maps. The land use land cover data were obtained from NASA website using Landsat 7 of the United state geological survey (USGS). The soil data was obtained from Somalia Water and Land Information Management (SWALIM) from FAO soil database.
Data analysis and quality test Rainfall-runoff modeling requires accurate rainfall input data. Satellite rainfall estimates (SREs) may serve as an alternative data source for model inputs, as they provide rainfall datasets at various temporal and spatial coverage, including ungauged areas. However, satellite rainfall estimate subjected to substantial biases. Hence, it is necessary to either minimize or remove the bias before the SREs can be used in any subsequent applications  (Goshime et al. 2020; 2019). Accordingly, homogeneity and consistency test were performed using nondimensional-zed and double-mass curve, respectively to verify the quality of dataset before.
Model calibration and Validation Observed flow data were used for model calibration. The streamflow data from the year 2005 to 2013 were used for model calibration and from 2014-2018 were used for validation. To aid model calibration, sensitivity analysis was performed to define influential parameters. Parameters such as CN, Lag, Muskingum (K and x) were selected for HEC-HMS model calibration and Manning's roughness coefficient (n), which is used to reflect the resistance to flow from the river bed and floodplain at each cross-section were used for HEC-RAS calibrated model parameter. The manning roughness values for flood plains is different from values for channels. A river channel is an area with its bed composed of firm soil, gravel, cobbles, boulders, or bedrock and the channel remains relatively unchanged throughout most of the range in flow.
4 ). The ranking based on the P-value and Chi-squared at a 5% level of significance (P<0. obtained the first rank but in the case of Chi-square test ranked second. Hence, to select the best fit distribution, a criterion that is the combination of total test scores were obtained from each dataset and with all 5 probability distributions. Based on the result of the distribution of the model estimated peak flow using GEV distribution with different return period's interval 5, 10, 20, 50, 100, and 200 with the result of 298 m 3 s -1 , 325 m 3 s -1 , 351 m 3 s -1 , 384 m 3 s -1 , 409 m 3 s -1 , and 434 m 3 s -1 , respectively (Fig. 7 ). Figure  8  illustrates the water depth and velocity of the flood without mitigation measures from HEC-RAS with analysis of all conditions. The GIS tool was used to generate mapping of the flood area.
Peak discharge was assigned to calculate among the 14-years of series data with unsteady flow analysis was carried out successfully for all cross-section and boundary conditions of the Shabelle river. The overall area inundated revealed an area of 3. 5km 2 , which means the flood submerges most part of the town. Approximately 80% of the floodwaters are below a depth of 1. 85 meters, while the rest of the places are deeper. Area of 1. 8 km 2 the flood depth approximate is 0. 2 up to 1. 5m, which revealed the river depth of 7. 53m. In the floodplain, the highest depth and velocity were 1. 85m, 7. 53m, and 3. 36ms -1 , 6. 03ms -1 , respectively. We note that some locations along the river banks have flashed close to joining each other and the extent of the flood is high, which indicate the need of mitigating measures.