dataesr/scientific-paragraphs-categorization huggingface dataset:
The "activity data" collected suffer from incompleteness, which led us to propose an innovative step, named "data extrapolation" to fill gaps, based on 4 processes, e. g. , analogies, crop modelling, expert knowledge and literature findings. First, this study allowed us to characterise, in detail, the farming systems of the Kairouan irrigated plain. Nine typical farming systems archetypes were identified which are related to (i) corporate agriculture (all with good access to water but differing in their production), (ii) family farming (differentiated according to water access and their production) and (iii) landless farmers (livestock breeders). The categories above accounted for three, five and one farming system archetypes, respectively. Activity data of seventy cropping and livestock systems encountered in these nine farming systems were collected through interviews and completed with the extrapolation process.
Second, the new ASD-based LCI methodology was assessed with regards to our objective of uncertainty reduction on LCA input data. The pedigree matrix approach was used in LCA to compare uncertainty of input data obtained using 3 data collection protocols, i. e. , i. ) the full ASD-based LCI methodology (including stratified sampling and extrapolation), ii. ) partial ASD-based methodology (including stratified sampling but without extrapolation) and iii. ) classical statistics-based input data. This work showed that stratified sampling played a key role in reducing LCI data uncertainty: uncertainty (when compared to the ideal case) was halved when switching from the statistics-based LCI to the ASD-based LCI using stratified sampling (and no extrapolation). When the full ASD-based LCI methodology was applied (i.
Acknowledgements This study was conducted within the framework of a multilateral  Africa-Europe  project , Eau4Food  (www. eau4food. info/),which aims to increase food production in irrigated areas in Africa while ensuring healthy and resilient environments. Ludivine Pradeleix ,  Véronique Bellon-Maurel ,  Sami Bouarfa  and  Philippe Roux  are members of the  ELSA-LCA research group (Environmental Life Cycle and Sustainability Assessment , www. elsa-lca. org);they also thank all the members of  ELSA  for their valuable advice.
Thus, more localised analyses incorporating key social dimensions of vulnerability are required to better understand how combined impacts to fisheries and agriculture may affect coastal communities. Here, we combine a measure of exposure based on model projections of losses to exploitable marine biomass (here dubbed fisheries catch potential) and agriculture from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 dataset with a measure of sensitivity based on survey data about material wealth and engagement in fisheries, agriculture, and other occupational sectors from >3,000 households across 72 tropical coastal communities in five countries (see Supplementary Data file). We answer the following questions: 1) What are the potential impacts of projected changes to fisheries catch potential and agriculture on coastal communities?, 2) How much will mitigation measures reduce these potential impacts?, and 3) Are lower socioeconomic status coastal communities facing more potential impacts from climate change than their wealthier counterparts? We show that: fisheries tend to be more impacted than agriculture although there is substantial within-country variability; climate change mitigation can reduce the number of locations experiencing a double burden (i.
Unfortunately, indicators of these dimensions of adaptive capacity were not collected in a standardised manner across all of the different projects comprising this study. Fifth, we investigated the potential impacts of climate change on two key food production sectors, but there may be other climate change impacts which have much more profound impacts on people's wellbeing. For example, sea level rise may destroy homes and other infrastructure  43  , while heat waves may result in direct mortality  44. Last, we used shared socioeconomic pathway exploratory scenarios that bracket the full range of scenario variability (SSP5-8. 5 and SSP1-2. 6). At the time of publication, these were the only scenarios available for both fisheries and agriculture using the ISIMIP Fastrack Phase 3 dataset. Future publications may wish to explore additional scenarios.
Methods Sampling of coastal communities. Here, we integrated data from five different projects that had surveyed coastal communities across five countries  [47] [48] [49] [50]. Between 2009 and 2015, we conducted socioeconomic surveys in 72 sites from Indonesia (n = 25), Madagascar (n = 6), Papua New Guinea (n = 10), the Philippines (n = 25), and Tanzania (Zanzibar) (n = 6). Site selection was for broadly similar purposes-to evaluate the effects of various coastal resource management initiatives (collaborative management, integrated conservation and development projects, recreational fishing projects) on people's livelihoods in rural and peri-urban villages. Within each project, sites were purposively selected to be representative of the broad range of socioeconomic conditions (e. g. , population size, levels of development, integration to markets) experienced within the region.
We did not survey strictly urban locations (i. e. , major cities). Because our sampling was not strictly random, care should be taken when attempting to make inferences beyond our specific study sites. We surveyed between 13 and 150 households per site, depending on the population of the communities and the available time to conduct interviews per site. All projects employed a comparable sampling design: households were either systematically (e. g. , every third house), randomly sampled, or in the case of three villages, every household was surveyed (a census) (see Supplementary Data file). Respondents were generally the household head, but could have been other household members if the household head was not available during the study period (i. e. was away). In the Philippines, sampling protocol meant that each village had an even number of male and female respondents.
These surveys were semi-panel data (i. e. the community was surveyed repeatedly, but we did not track individuals over each sampling interval) and sometimes occurred in different seasons. For illustrative purposes, we plotted how these villages changed over time along the first two principal components. Sensitivity. We asked each respondent to list all livelihood activities that bring in food or income to the household and rank them in order of importance. Occupations were grouped into the following categories: farming, cash crop, fishing, mariculture, gleaning, fish trading, salaried employment, informal, tourism, and other. We considered fishing, mariculture, gleaning, fish trading together as the 'fisheries' sector, farming and cash crop as the 'agriculture' sector and all other categories into an 'off-sector'.
As with the material style of life, we also conducted an exploratory analysis of how joint agriculture-fisheries sensitivity has changed over time in a subset of sites (Muluk and Ahus villages in Papua New Guinea) that have been sampled since 2001/2002  52. Although our survey methodology has the potential for bias (e. g. people might provide different rankings based on the season, or there might be gendered differences in how people rank the importance of different occupations  53  ), our time-series analysis suggest that seasonal and potential respondent variation do not dramatically alter our community-scale sensitivity metric. Exposure. To evaluate the exposure of communities to the impact of future climates on their agriculture and fisheries sectors, we used projections of production potential from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 experiment dataset of global simulations.
Sensitivity test. To determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4746 sites that were randomly selected from 47,460 grid cells within 1 km of the coast of the 5 countries we studied which had population densities >25 people/km 2 , based on the SEDAC gridded populating density of the world dataset (https://sedac. ciesin. columbia. edu/ data/set/gpw-v4-population-density-rev11/data-download). We used Cohen's D to determine the size of the difference between our sites and the randomly selected sites. Validating ensemble models. We attempted a two-stage validation of the ensemble model projections. First, we reviewed the literature on downscaling of ensemble models to examine whether downscaling validation had been done for the ecoregions containing our study sites.
While no fisheries ensemble model downscaling had been done specific to our study regions, most of the models of the ensemble have been independently evaluated against separate datasets aggregated at scales down to Large Marine Ecosystems (LMEs) or Exclusive Economic Zones (EEZs) (see  11  ). For example, the DBEM was created with the objective of understanding the effects of climate change on exploited marine fish and invertebrate species 2,70. This model roughly predicts species' habitat suitability; and simulates spatial population dynamics of fish stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield  2, 62, 71. Compared with spatially-explicit catch data from the Sea Around Us Project (SAUP; www. seaaroundus. org)  70 there were strong similarities in the responses to warming extremes for several EEZs in our current paper (Indonesia and Philippines) and weaker for the EEZs of Madagascar, Papua New Guinea, and Tanzania.
At the LME level, DBEM MCP simulations explained about 79% of the variation in the SAUP catch data across LMEs  72. The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% confidence interval of the linear regression relationship  62. Another example, BOATS, is a dynamic biomass size-spectrum model parameterised to reproduce historical peak catch at the LME scale and observed catch to biomass ratios estimated from the RAM legacy stock assessment database (in 8 LMEs with sufficient data). It explained about 59% of the variability of SAUP peak catch observation at the LME level with the Agulhas Current, Bay of Bengal, and Indonesian Sea catches reproduced within +/-50% of observations  61. The EcoOcean model validation found that all four LMEs included in this study fit very close to the 1:1 line for overserved and predicted catches in 2000  64, 65.
We then attempted to validate the models in our study regions. For the crop models, we examined production-weighted agricultural projections weighted by current yields/production area (Supplementary Fig. 1 ). We used an observational yield map (SPAM2005) and multiplied it with fractional yield time series simulated by the models to calculate changes in crop production over time, which integrates results in line with observational spatial patterns. The weighted estimates were not significantly different to the unweighted ones (t = 0. 17, df = 5, p = 0. 87). For the fisheries models, our study regions were data-poor and lacked adequate stock assessment data to extend the observed global agreement of the sensitivity of fish biomass to climate during our reference period (1983-2013). Instead, we provide the degree of model run agreement about the direction of change in the ensemble models to ensure transparency about the uncertainty in this downscaled application.
Acknowledgements J. E. C. is supported by the  Australian Research Council  ( CE140100020 ,  FT160100047 ,  DP110101540 , and  DP0877905 ). This work was undertaken as part of the  Consultative Group for International Agricultural Research (CGIAR) Research Program on Fish Agri-Food Systems (FISH)  led by  WorldFish. T. D. E acknowledges support from the  Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2021-04319 ). M. C. and J. S. acknowledge support from the  Spanish  project  ProOceans  ( RETOS-PID2020-118097RB-I00 ) and the  'Severo Ochoa Centre of Excellence ' accreditation ( CEX2019-000928-S)  to the  Institute of Marine Science (ICM-CSIC ). G. G. G. acknowledges support from an  Australian Research Council   Discovery Early Career Research Award  ( DE210101918 ).
Data availability The de-identified exposure, sensitivity, and material style of life data generated in this study for each community can be accessed through Zenodo  76  [https://doi. org/10. 5281/ zenodo. 6496413]. All outputs from the FishMIP model ensemble are available via ISIMIP [https://www. isimip. org/gettingstarted/data-access/]. Raw social survey data are not available because our verbal informed consent made it clear that only aggregated data would be published. The sample sizes and proportions of each community included in the social surveys can be found in the Supplementary Data file. Base layer map data in Fig. 1c  and Supplementary Figures  5, 8 , and 9 is from Natural Earth, which is freely available through their website (naturalearthdata. com). The SEDAC gridded populating density of the world dataset used to identify a subset of random locations can be found at the following: https://sedac.
Satellite data acquisition Sea surface chlorophyll (SSC) data at a daily and 4. 5 km resolution were downloaded from MODIS (http://oceancolor. gsfc. nasa. gov) and were used to calculate 5-day averages to obtain a proxy of surface oceanic primary production for the period from 18/09/2016 to 07/12/2016. Delayed-time mean sea level anomalies (MSLAs) at a daily and 1/4° resolution were produced and distributed by the Copernicus Marine Environment Monitoring Service project (CMEMS) and available at http://marine. copernicus. eu/. MSLAs were used to describe the mesoscale eddy field at the time of MAD-Ridge cruise.
Acknowledgements The authors acknowledge the work carried by the non-scientific staff on board the  R/V ANTEA  and the scientific staff onboard and on land, including  Yves Cherel  ( CNRS, La Rochelle, France ),  Delphine Thibault  ( MIO, Marseille, France ) and  P. Alexander Hulley  ( South Africa ) for identifying the micronekton taxa, and  Hervé Demarcq  ( IRD, Sète, France ) for providing the SSC data. The authors also acknowledge the assistance and support of the chief scientists  Francis Marsac  for  La Pérouse ,  Jean-François Ternon  for  MAD-Ridge cruises (IRD, Sète, France ) and  Sebastien Jaquemet  ( Université de la Réunion ) for IOTA cruise, and the  Master BEST-ALI  students who helped with the collection of samples. The authors further appreciate the assistance of  Gaël Le Croizier  for the mercury analyses conducted at the  GET (Toulouse, France)  and  François Le Loc 'h for the stable isotope analyses at the  Pôle de Spectrométrie Océan (Plouzané, France).
The data collection was mainly supported by the  Flotte Oceanographique Française (French Oceanographic Fleet ) and IRD in relation to the logistics of the  R/V ANTEA. Additional funding was received from the  Conseil Régional de la Reunion (Reunion Regional Council ,  Bop 123/2016 ) for La Pérouse cruise, and from the  Fonds Français pour l'Environnement Mondial (FFEM)  as part of the  FFEM-SWIO  project  on Areas Beyond National Jurisdiction  (ABNJ) of the  South West Indian Ocean for MAD-Ridge cruise. This work was financially supported by the  French National Research Agency  project  ANR-17-CE34-0010  MERTOX (PI David Point). Pavanee Annasawmy is the beneficiary of a MARG-I bursary granted by the  WIOMSA (Western Indian Ocean Marine Science Association ) and the  EuroMarine Network 's (http://www.
Satellite Data The Ocean Land Color Instrument's (OLCI) satellite sensor, which is onboard the Sentinel-3 satellite platform, provides images at a spatial resolution of 300 m for 21 spectral bands from the visible to near infrared, namely from 400 to 1020 nm. OLCI is highly appropriate for providing reliable ocean color observations thanks to its high radiometric sensitivity. Two satellite platforms were launched in 2016 and 2018, respectively, providing a 1-day temporal resolution  [17]. Five Sentinel-3/OLCI images were analyzed for this study; they were acquired on 8 July 2017 13:00, 27 June 2018 14:24, 9 May 2020 14:00, 14 September 2020 14:20 and 28 December 2020 13:55. These images were selected because they exhibit numerous occurrences of Sargassum and were captured during different years and seasons.
In Situ Data In situ measurements are important to correctly interpret satellite observations, especially to validate the proposed adjustment of the atmospheric correction procedure in the presence of Sargassum. Although in situ data were not collected for the current study,  Ody et al. [18]  were able to gather in situ data of Sargassum aggregations through two cruise campaigns in the western Atlantic Ocean between Brazil and the Caribbean Sea in the summer and autumn of 2017, which is the same period as the satellite image processed here. The above water reflectances were collected by Ody et al. using deck observations of Sargassum aggregations. Water reflectances were recorded for many Sargassum aggregations associated with their abundance (fractional coverage at the metric scale  [12] ).
In Situ Data In situ measurements are important to correctly interpret satellite observations, especially to validate the proposed adjustment of the atmospheric correction procedure in the presence of Sargassum. Although in situ data were not collected for the current study,  Ody et al. [18]  were able to gather in situ data of Sargassum aggregations through two cruise campaigns in the western Atlantic Ocean between Brazil and the Caribbean Sea in the summer and autumn of 2017, which is the same period as the satellite image processed here. The above water reflectances were collected by Ody et al. using deck observations of Sargassum aggregations. Water reflectances were recorded for many Sargassum aggregations associated with their abundance (fractional coverage at the metric scale [12]).
Extension of the POLYMER Algorithm to Sargassum-Dominated Waters The spectral reflectance of pure Sargassum (Figure  3 ) is highly similar to land vegetation reflectance. The main feature is observed in the NIR bands where the reflectance strongly increases. A lower increase of the reflectance is observed in the green. Absorption properties of Sargassum can be observed through the decrease of the reflectance in the blue and the red bands. Although the proportion of Sargassum content in a given pixel is weak in the open ocean, typically lower than 5% as revealed from MODIS satellite data analysis using the monthly mean of the AFAI index [7], water reflectance remains high, especially in the NIR domain. As highlighted in Section 2. 4. 1, the aerosol reflectance is commonly derived from the reflectance measured in the NIR domain where the water reflectance is supposed to be negligible.
e. , increase from visible to NIR bands) vanish. A reduction of between 75% and 88% of the pixels showing a negative water reflectance above 600 nm were noticed after the correction of the five images. The efficiency of the correction is fairly related to the cloud coverage. The more clouds, the less effective is the method. The comparison between the in situ data and the water reflectance derived over Sargassum areas based on the improved atmospheric correction corroborates the relevance of the proposed method. Therefore, relevant estimates of Sargassum variables such as biomass and fractional coverage could be further derived based on the methodology developed in this study. This method can also be applied to other satellite sensors, such as Aqua/MODIS, Terra/MODIS, SUOMI-NPP/VIIRS and Sentinel-2/MSI.
, 2014). Indeed, the HyMeX special observation periods are, to date, the most ambitious and comprehensive observing campaigns internationally coordinated to investigate the water cycle in the Mediterranean  (Ducrocq et al. , 2014). In later years, new modelling capabilities and observational datasets were made available to the scientific community, while new results from interdisciplinary studies emerged. Such results also addressed fundamental questions on cyclone interactions with the Mediterranean Sea and aerosols, showing a strong effect of cyclones on the regional environment and its water cycle, in addition to their socioeconomic impact. Scientific research in the broader field of Mediterranean cyclones is currently advancing rather asymmetrically. For instance, numerous studies have recently been devoted to tropical-like cyclones, known in the scientific literature as medicanes, a portmanteau of the words "Mediterranean hurricanes"  (Emanuel, 2005; Miglietta, 2019).
Climatological perspective of Mediterranean cyclones Mediterranean cyclones represent a very distinct element of the global climate system. Several studies between the 1950s and 1960s brought to light the high frequency of cyclogenesis in the Mediterranean  (Pisarski, 1955; Petterssen, 1956; HMSO, 1962; Radinović, 1965). Notably, the analysis of Petterssen indicated the Mediterranean as the most active cyclogenetic area in the Northern Hemisphere in winter. In later years, the effort to unveil the climatological aspects of Mediterranean cyclones continued through the use of gridded datasets and objective identification methods. This section provides an overview of the main results of these efforts, focusing on the climatology of cyclone tracks, the different cyclone types that develop in the region and their future trends.
2. 1 Spatial variability in and seasonal cycle of Mediterranean cyclone tracks  Alpert et al. (1990)  performed one of the first studies focusing on the climatology of Mediterranean cyclones. The authors used a 5-year  (1983) (1984) (1985) (1986) (1987)  climatological dataset of ECMWF operational analyses with a grid spacing of 2. 5 • × 2. 5 • and a time interval of 12 h. The monthly variations in cyclonic tracks in the entire Mediterranean showed a pronounced seasonal cycle. Cyclone occurrences are more frequent in winter, with tracks located along the northern and central part of the Mediterranean basin, while in summer, cyclone occurrences become frequent over North Africa. In later years, a high number of studies were devoted to producing a climatology of cyclones, with various results on the spatial and temporal variability in their tracks (e.
Such thermal shallow depressions, confined to the atmospheric boundary layer, should not be confused with active and deeper cyclones. Indeed, the summer position of the polar jet renders upper-tropospheric systems less likely to intrude on the Mediterranean and trigger cyclogenesis  (Flaounas et al. , 2018). The actual number of Mediterranean cyclones varies significantly among climatological studies from hundreds to even thousands of occurrences per year. Such high uncertainty on the number of tracks in the Mediterranean is related to the sensitivity of the cyclone tracking methods, the criteria used to identify cyclones and the properties of the input dataset (e. g. grid spacing, temporal interval of fields). It is thus not surprising that the actual number of Mediterranean cyclones and the characteristics of their tracks are highly dependent on the cyclone tracking method  (Neu et al.
Cyclones forming over the Atlantic Ocean It is not uncommon for cyclones forming over the Atlantic to intrude into the Mediterranean region and cause substantial damage  (Nissen et al. , 2010). Such an example is storm Klaus that intruded into the Mediterranean in January 2009  (Liberato et al. , 2011). In a more recent study,  Lionello et al. (2016)  used 14 different cyclone tracking methods from the IMILAST dataset  (Neu et al. , 2013)  and showed that the Mediterranean cyclones that form within the Mediterranean represent 61 %-85 % of the total Mediterranean tracks.