MétaCan
Menu
Back to cohort
Record W2904369116 · doi:10.5539/ijef.v11n1p66

Indoor Air Pollution and Respiratory Diseases in Rural Areas of North and Northeast Brazil

2018· article· en· W2904369116 on OpenAlexvenueno aff
Giovani Baggio, César Augusto Oviedo Tejada, Anderson Moreira Aristides dos Santos, Lívia Madeira Triaca

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthAsthmaIndoor air qualityChronic bronchitisPollutionLogistic regressionAir pollutionBronchitisPopulationWork (physics)GeographyEnvironmental protectionSocioeconomicsEnvironmental scienceMedicineEnvironmental engineeringEconomicsEngineering

Abstract

fetched live from OpenAlex

Indoor pollution is a risk factor for several diseases, especially those affecting the respiratory system. In Brazil, the use of solid fuels for cooking is still very present. The smoke emitted in the environment by the burning of solid fuels is causing such pollution, and the poorest families are the most affected. The objective of this research was to study the association between respiratory diseases (bronchitis and asthma) and the use of solid fuels among inhabitants of rural areas of the North and Northeast regions of Brazil. The database used was the 2008 National Household Sample Survey (PNAD/IBGE), and the analyzes involved estimations through the Logistic regression. The results showed that women exposed to indoor pollution had a higher odd of having bronchitis or asthma. This effect was not significant in the case of men. This work indicates a path for the implementation of public policies aimed at raising public awareness about the health damages caused by the use of solid fuels and facilitating access to a cleaner source of energy for the low-income population.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.186

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Economics and FinanceSame topicEnergy and Environment ImpactsFrench-language works237,207