dataesr/scientific-paragraphs-categorization huggingface dataset:
e. from beginners to intermediate and expert status) as other programs do to favor knowledge and skills acquisition, for instance the 'iSpot' program  (Silvertown et al. 2015). However, even in this last case, the social recognition of participants' skills seems to depend primarily on volunteers' online activity  (Silvertown et al. 2015). This result is coherent with our findings that participants who realize most collections (that is, most skilled participants) post the highest number of comments (Figure  1 ). Second, volunteers contribute to the development and/or diffusion of knowledge concerning biological topics, such as the effect of environmental changes on plants-insects' interactions, the functions and uses of plants, or insects' behavior. As such, Spipoll does not only promote collective identification learning, but also the diffusion and discussion of knowledge about a diversity of biological topics.
This dynamics is promoted by the sharing of numerous external resources. These resources might help volunteers to improve both their identification skills and their knowledge of natural history. Third, our comments' content analysis shows the emergence of a cognitive interest, from individuals, for the nature of the research procedures. This dynamics might enhance collective learning. To illustrate that point, one may refer here to the group of comments which insist on the importance of respecting Spipoll's protocol; to the group of comments which provide reflections about environmental and technical factors that may influence data collection; or to the comments which propose ways to improve Spipoll's protocol. Besides, our comments' content analysis shows that Spipoll volunteers sometimes formulate hypothesis and original questions on the basis of observed phenomena.
2018). Spipoll is accessible to a diverse (urban or rural) audience, and the produced collections provide the scientists with a vast quantity of data. Spipoll program have led to numerous scientific publications in peer-review journals, concerning for instance urbanization's effects on community composition  (Deguines et al. 2016) , contrasted affinities of pollinators with different land-use  (Deguines et al. 2012) , the role of domestic gardens as favorable pollinator habitats in impervious landscapes  (Levé, Baudry, and Bessa-Gomes 2019)  or floral morphology as the main driver of flowerfeeding insect occurrences in the Paris region  (Desaegher et al. 2018 ). These productions suggest that the Spipoll CS program reach the objective of collecting reliable and usable data. However, not all CS programs achieve this objective of collecting datasets that can be used in robust scientific studies  (Bird et al.
Data and Methods Our data come from the 2008-2009 French TeO survey and the 2011 Canadian NHS. TeO comprised over 21,000 interviews, including oversampling of immigrants and minority populations. The NHS, conducted on the scale of a 20 percent census but still a voluntary survey like the TeO, had 6,719,000 respondents: 1,604,000 in Québec and 5,115,000 in English Canada. Completed within three years of each other, these surveys capture comparable economic conditions (as a robustness test, we replicated our Canadian analysis in the 2001 census, with similar results). We restrict the analysis to adults aged 25-60 to focus on those with completed education and capable of independence, though not retired. To facilitate analysis of households, we exclude same-sex couples and couples living with parents, about 1 percent in both countries.
Non-Muslims from the Middle East/ West Asia had incomes 8 percent below the mainstream, compared to 32 percent for Muslims. Additional variables (Table  2 ) include age, educational attainment, urban area of residence (metropolitan areas with significant immigrant minority concentration: Toronto and Vancouver in English Canada; Montreal in Québec; Paris, Lyon, and Marseille in France), gender, labor force status (whether nonzero earnings were reported), marital status (whether in a couple), other household composition variables, and citizenship status. Educational attainment is measured in seven categories defined similarly in French and Canadian data and with a similar relation to incomes: no qualifications, less than secondary school, secondary school, postsecondary vocational, postsecondary academic, bachelor's degree, and higher degree.
Thus, more localised analyses incorporating key social dimensions of vulnerability are required to better understand how combined impacts to sheries 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 " sheries 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 sheries, agriculture, and other occupational sectors from > 3,000 households across 72 tropical coastal communities in ve countries (Table  S1 ). We ask: "What are the potential impacts of projected changes to sheries catch potential and agriculture on coastal communities?" "How much will mitigation measures reduce these potential impacts?" and "Are lower socioeconomic status coastal communities facing more potential impacts from climate change than their wealthier counterparts? ".
At the time of publication, these were the only scenarios available for both sheries and agriculture using the ISIMIP Fastrack Phase 3 dataset. Future publications may wish to explore additional scenarios. Our study quanti es the potential impacts of climate change on key food production sectors in tropical coastal communities across a broad swath of the Indo-Paci c. We nd that both exposure and sensitivity to sheries is generally higher than to agriculture, but some places may experience losses from both sectors simultaneously. These losses may be compounded by other drivers of change, such as over shing or soil erosion, which is already leading to declining yields  45, 46. Simultaneous losses to both sheries catch potential and agriculture will limit people's opportunity to adapt to changes through switching livelihoods between food production sectors  9.
Sampling of coastal communities Here, we integrated data from ve different projects that had surveyed coastal communities across ve 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 shing 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.
Exposure To evaluate the exposure of communities to the impact of future climates on their agriculture and sheries 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. Production potential of agriculture and sheries for each of the 72 community sites and 4,746 randomly selected sites from our study countries with coastal populations > 25 people/km 2 were projected to the mid-century (2046-2056) under two emission scenarios (SSP1-2. 6, and SSP5-8. 5) and compared with values from a reference historical period (1983-2013). For sheries exposure (E F ), we considered relative change in simulated total consumer biomass (all modelled vertebrates and invertebrates with a trophic level > 1).
Sensitivity Test To determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4,746 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-populationdensity-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 sheries ensemble model downscaling had been done speci c 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 sh and invertebrate species  2, 71. This model roughly predicts species' habitat suitability; and simulates spatial population dynamics of sh stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield and maximum yield estimated from stock assessment models (R 2 of 0.
74  show that most crop models and the ensemble mean are capable of reproducing the weather-induced yield variability in countries with intensely managed agriculture. In countries where management introduces strong variability to observed data, which cannot be considered by models for lack of management data time series, the weather-induced signal is often low  75  , but crop models can reproduce large shares of the weather-induced variability, building trust in their capacity to project climate change impacts  74. 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 (Supplemental 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.
7262,70. Compared with spatially explicit catch data from the Sea Around Us Project (SAUP; www. seaaroundus. org)71there were  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 LMEs72. The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% con dence interval of the linear regression relationship62. 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 su cient data).
Acknowledgments. 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. M. C and JS 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 ). CMP acknowledges support from  NOAA  grants  NA20OAR4310441  and  NA20OAR4310442.
We focus on pre-Columbian agricultural regimes in the Maya Lowlands, using new datasets of archaeological wood charcoal, seeds, phytoliths, and starch grains; biological properties of plants; and contemporary Indigenous practices. We address inherited models of agriculture in the lowlands: the limitations of the environment (finding more affordances than anticipated by earlier models); the homogeneity of agricultural strategies (finding more heterogeneity of strategies across the lowlands than a single rigid template); the centrality of maize in agriculture (finding more reliance on root crops and tree crops than historically documented); the focus on the milpa system as food base (finding more agroforestry, homegardening, horticulture, and wild resource management than previously documented); the dominance of swidden strategies in agricultural practices (finding more diverse practices than accounted for in most models); and the foregrounding of maize crop failure in collapse models (finding more evidence of resilience and sustainable agricultural practices than predicted).
In this reframing, ongoing labor regimes, daily subsistence practices, movement of foodstuffs in tax and trade, responses to environmental challenges, social strife related to crop failure, and abandonment of communities are all implicated. In the following passages, we offer general context for the Maya Lowlands, an area encompassing a number of vegetation communities and environmental conditions, from evergreen rainforests, to seasonally dry forests, to semi-dry savannahs. We present traditional understandings of agricultural practice in the Maya Lowlands, understandings grounded frequently in ethnohistoric and ethnographic research from the Spanish colonial era through the 1980s. We then synthesize contemporary approaches to revisit old questions, drawing on new datasets from analyses of archaeological wood charcoal, seeds, phytoliths, and starch grains; biological properties of plants including pollination regimes; and contemporary Indigenous uses of food plants.
in press). We present an in-depth review of literature published since the 1990s, when ancient agricultural research in the Maya area saw a turning point in approaches and datasets. We focus on published data from the Formative through the Classic periods, sourced from across the Maya Lowlands (Fig. 1 ), although studies from larger city centers, Classic period occupations, and the southern lowlands are much more robustly represented in the available literature. This review of over 120 studies with paleoethnobotanical data allows us to develop new perspectives on agricultural techniques of the Formative and Classic periods, and to discuss the implications of revised agricultural modeling on current collapse models. Comparing this new synthesis with earlier understandings of agricultural practice, we illuminate the general heterogeneity of strategies that Maya people used in the lowlands as they negotiated dynamic socio-environmental contexts.
However, we do not address specific spatial-temporal patterns of plant cultivation, domestication, or diffusion. These are different research questions that require extensive datasets for each plant to be addressed, dating back to at least the Archaic period, and such datasets are not yet available. Comparing this new synthesis with earlier understandings of pre-Columbian agricultural practice, we illuminate the general heterogeneity of strategies that Maya people used in the lowlands as they negotiated social contexts. Despite this overall heterogeneity, we see a few general trends in recent datasets that raise new questions and methodological challenges for future research, regarding 1) characterizations of lowland Maya environments (affordances and limitations); 2) homogeneity of agricultural strategies (milpa, etc.
Later archaeologists attended to these historic accounts, and agricultural practices held particular importance in initial studies in the Maya area. In the mid-20th century, archaeologists developed a rich set of analogies to apply to ancient Maya practices by using ethnographic and historical literature. Unfortunately, such analogies were often applied wholesale in studies of ancient Maya agriculture. For many years, early ethnographic and ethnohistoric descriptions of agriculture served as the starting point for discussion, whether or not such frameworks were then critically reassessed using updated datasets. Many early works began with Friar Diego de Landa's 16th century account of The Things of Yucatan. De Landa described Yucatan as ". the country with least earth that I have seen, since all of it is one living rock.
But new techniques and hypotheses also began to expand knowledge in other directions. Some early scholars signaled caution in using the direct-historic approach to construct usable analogies, and models began to emerge that were experimental (e. g. ,  Puleston 1971 ) and cross-culturally comparative (e. g. ,  Drucker and Fox 1982). Types of archaeological evidence began to include palynological profiles (e. g. ,  Turner II and Harrison 1981) , isotopic signatures (e. g. ,  White and Schwarcz 1989) , and chemical residues (e. g. ,  Hurst et al. 1989) , as well as amplified datasets from paleoethnobotanical research and settlement studies (e. g. ,  McClung de Tapia 1985; Sheets 1982; Zier 1980). Types of archaeological queries began to shift from description toward systems approaches (e. g. ,  Sharer 1977) , cultural ecology (e.
Shifts in data collection have included where and how we sample (see D'Alpoim  Guedes et al. 2015) , with scholars targeting more households and smaller sites (vs. large city centers), engaging in more blanket sampling (vs. simply targeting hearths and tombs), and exploring more rural areas (for agricultural features and soil chemistry studies). Some traditional methods in data collection, including GIS and spatial archaeology, soil studies, isotopic analysis, usewear studies, ecology modeling, and ethnographic data, have undergone a sharp uptick and more refinement, as methods have improved. Other datasets have also seen significant shifts, as analyses of phytoliths, starch grains, wood remains, paleofire signals, plant genetics, and plant biology have been initiated or augmented with advancements in sister fields.
All the key cultivated taxa have high visibility in the microbotanical record, as all have diagnostic starch grains (Fig. 6 ). Several taxa also have diagnostic phytoliths (e. g. , arrowroot [Maranta] and lerén  [Calathea] ). Over the past few decades, studies of microbotanical remains have yielded a rich array of these starchy underground food resources (Supplemental Table  1 ), complementing macrobotanical datasets and revealing the prominence of other types of crops in past Maya foodways. Far from famine food, manioc (Manihot esculenta; Fig. 7a-c ), a woody perennial, appears to have a key role in ancient Maya cuisine. Found at archaeological sites across southeastern Mesoamerica, the Caribbean, and through southern Central America  (Hather and Hammond 1994; Lentz et al. 1996; Morell-Hart 2014; Piperno and Holst 1998) , manioc appears to have varied regionally in importance as a food resource among Maya communities.
These combined data indicate that geophyte crops, far from a rarity, are found almost everywhere that microbotanical studies have yielded diagnostic remains, in addition to several places that have yielded more fragile macrobotanical remains. The high ubiquity of geophyte crops in microbotanical studies-where they preserve well for identification-indicates the likely high reliance placed on them by Maya farmers in the lowlands  (Dunning et al. 2018 (Dunning et al. , 2020;; Fedick 2017 Fedick , 2020;; Hather and Hammond 1994). In some cases, geophyte crops dominate assemblages while maize is secondary (e. g. ,  Sheets et al. 2012; Slotten et al. 2020). Critical future work could address spatial-temporal patterns of geophyte crop diffusion and intensity of use, as paleoethnobotanical datasets become more robust and microbotanical studies become more common.
Boosting valued tree species or discouraging the less preferred ones is a longlasting practice of forest management that has been documented in contemporary communities like the Itza in northern Petén and the Lakantun in Chiapas  (Atran 1993; Ford and Nigh 2016). Although it would be premature to generalize, we nevertheless suggest that the trends highlighted here show that ancient Maya people implemented sustainable silvicultural practices, though these practices probably varied greatly over time and across space. When did this forest management begin, and did it lead to the development of extant "forest gardens"  (Campbell et al. 2006; Ford and Nigh 2016; Gómez-Pompa 1987) ? These are questions that require systematic examination of anthracological datasets covering long periods of time in multiple sites.
Maya Agriculture: Corroborations, Contradictions, Complementarities, Caveats The additions and refinements in data collection described in previous passages lead us to view anew the inherited models we outlined in earlier sections. We apply our data syntheses to prior understandings of limitations of lowland Maya environments, homogeneity of agricultural strategies across the lowlands, centrality of one crop (maize), centrality of the milpa system as food base, dominance of swidden strategies in agricultural practices, and emphasis on crop failure in collapse models. In our review of literature from 1990 to 2020, we see corroboration between some archaeological datasets, including evidence of wild and managed taxa in assemblages of both food plants and wood; diverse agricultural strategies in botanical residues as well as landscape features; and specific strategies such as arboriculture, identified through features as well as wood charcoal assemblages.
Complementarity between datasets is evident in, for example, the visibility of root crops uniquely offered by starch grains and phytoliths (Fig. 6 ), the visibility of fuel harvesting and deforestation uniquely identified through wood residues and pollen, and the heightened visibility of water management presented by canal features. We also see a few contradictions between datasets, including differing tempos in correlations between agricultural strategies and "crisis" periods, and differing understandings of swidden practices in terms of ancient types used and their environmental effects. These latter contradictions, however, are likely related to the relationships of ancient farming practices to particular topographies, moisture regimes, and other factors at different points in time. We now consider how models prior to the 1990s stand in regard to analyses carried out over the past three decades.
Models inherited from earlier scholarly studies have faced several serious challenges over the past 30 years, while other models have held up under scrutiny and new datasets. Following our synthesis of research of the past 30 years, we reevaluate six key themes in agricultural modeling: characterizations of lowland Maya environments (affordances and limitations), homogeneity of agricultural strategies (milpa, etc. vs. managed mosaic), centrality of maize in subsistence and agriculture (vs. root crops, tree crops, and non-domesticates), centrality of the milpa system as food base (vs. incorporation of agroforestry, homegarden horticulture, wild collection), preponderance of swidden strategies in agricultural practices (vs. terraforming terraces and canals, enhancing soils), and dominance of maize crop failure in societal collapse models.