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Record W4401807846 · doi:10.21149/15853

Inseguridad alimentaria y del agua

2024· article· es· W4401807846 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSalud Pública de México · 2024
Typearticle
Languagees
FieldEnvironmental Science
TopicPublic Health and Environmental Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsGeographyHumanitiesPolitical scienceCartographyArt

Abstract

fetched live from OpenAlex

OBJETIVO: Analizar la inseguridad del agua (IAg), frecuencia del suministro de agua (FSA) e inseguridad alimentaria (IA) en hogares mexicanos, abordando sus determinantes sociales y aportar recomendaciones para las políticas públicas. Material y métodos. Se analizó la información de 28 500 hogares de la Encuesta Nacional de Salud y Nutrición (Ensanut Continua 2020-2023). Se aplicaron escalas de experiencias validadas como HWISE y ELCSA, para medir la IAg e IA, así como un indicador sobre FSA, de acuerdo con algunos determinantes. RESULTADOS: 16% de los hogares mexicanos experimentan IAg y 22% padecen IA moderada y severa. Sólo 34.7% recibe agua las 24 horas todos los días. Los determinantes de los hogares más afectados por la IAg, IA y en FSA son las peores condiciones de bienestar, ser indígena y cuando la IA e IAg se encuentran juntas en los hogares. CONCLUSIONES: Es imprescindible acelerar la aplicación y mejorar la cobertura de acciones que impacten positivamente en el acceso y disponibilidad de agua y alimentos de las personas vulnerables.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.020

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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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