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Climate variability, agricultural livelihoods and food security in Semiarid Brazil

2016· article· en· W2560512890 on OpenAlexaff
Patrícia Mesquita, Hannah Wittman, José Aroudo Mota

Bibliographic record

VenueSustainability in Debate · 2016
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFood securityLivelihoodSubsistence agricultureClimate changeGeographyContext (archaeology)Vulnerability (computing)AgricultureSustainabilityLivestockSocioeconomicsNatural resource economicsEcologyEconomicsForestry

Abstract

fetched live from OpenAlex

Climate change and variability are among the main threats to socio-ecological sustainability in many semi-arid regions of the world and are of special concern to resource-poor family farmers. In the Brazilian semi-arid region, high levels of social vulnerability in addition to predicted climate events can adversely affect subsistence crops and other cultivated areas with serious consequences for rural food security. An extreme drought that started in 2010 left 174 (of 184) municipalities in the northeastern state of Ceará, Brazil, in a situation of emergency in 2012. During the period of drought, we studied household production characteristics, sources of water for domestic consumption, perception of temperature change and the relationship of those variables with perceived food security. Food security was associated to the presence of piped water and to the diversity of livestock owned by the household. In addition to the importance of observing the role of those variables in public policies related to food security and regional development in the semi-arid region of Brazil, we also highlight the need of understanding the local context where those policies are implemented and the types of local adaptations being performed during periods of shock, which will be recurrent in a scenario of climate change.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.394
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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