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Record W3210441536 · doi:10.1038/s41597-021-01073-9

Attributes of the food and physical activity built environments from the Southern Cone of Latin America

2021· article· en· W3210441536 on OpenAlexfundno aff
Laura Gutiérrez, Natalia Elorriaga, Luz Gibbons, Santiago Melendi, Martín Chaparro, Matías Calandrelli, Fernando Laņas, Nora Mores, Jacqueline Ponzo, Rosana Poggio, Mabel Berrueta, Vilma Irazola

Bibliographic record

VenueScientific Data · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsLatin AmericansLimitingGeographyBuilt environmentIncentivePhysical activityHealthy foodEnvironmental healthWalkabilityActive livingEnvironmental qualityRegional scienceEnvironmental planningBusinessPolitical scienceEcologyMedicineEconomicsEngineering

Abstract

fetched live from OpenAlex

Previous studies have shown the influence of the physical and social environments on the development of obesity and non-communicable diseases (NCD). An obesogenic environment promotes higher dietary energy intakes and sedentary behaviors while limiting opportunities or incentives for active living. This paper presents a dataset with key attributes of the food and physical activity built environment, including green spaces, quality of streets and sidewalks, and different types of food retail outlets in four cities of the Southern Cone of Latin America. A total of 139 representative neighborhoods randomly selected from: Marcos Paz and Bariloche (Argentina), Temuco (Chile) and Canelones-Barros Blancos (Uruguay) were evaluated, where standardized community walks were conducted for direct observation of the built environment. This dataset will contribute valuable data to the evaluation of obesogenic environments in the region, and could be linked to additional ecological information about risk factors for NCDs and socio-economic features from other sources. Understanding environmental influences on cardiovascular risk factors and individual habits may help explain NCD outcomes and plan urban policies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
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 head0.061
GPT teacher head0.282
Teacher spread0.221 · 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 designBench or experimental
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

Citations12
Published2021
Admission routes1
Has abstractyes

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