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Record W1988477198 · doi:10.1080/09603123.2014.915017

BTEX air concentrations and self-reported common health problems in gasoline sellers from Cotonou, Benin

2014· article· en· W1988477198 on OpenAlexafffund
Honesty Tohon, Benjamin Fayomi, Mathieu Valcke, Yves Coppieters, Catherine Bouland

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité de Montréal
FundersUniversité de Montréal
KeywordsBTEXSocioeconomic statusEnvironmental healthGasolineMedicineChemistryWaste managementBenzeneXyleneEngineering

Abstract

fetched live from OpenAlex

To examine the relation between BTEX exposure levels and common self-reported health problems in 140 gasoline sellers in Cotonou, Benin, a questionnaire documenting their socioeconomic status and their health problems was used, whereas 18 of them went through semi-directed qualitative individual interviews and 17 had air samples taken on their workplace for BTEX analysis. Median concentrations for BTEX were significantly lower on official (range of medians: 54-207 μg/m³, n = 9) vs unofficial (148-1449 μg/m³, n = 8) gasoline-selling sites (p < 0.05). Self-reported health problems were less frequently reported in sellers from unofficial vs official selling sites (p < 0.05), because, as suggested by the semi-directed interviews, of their fear of losing their important, but illegal, source of income. Concluding, this study has combined quantitative and qualitative methodological approaches to account for the complex socioeconomic and environmental conditions of the investigated sellers, leading to their, in some cases, preoccupying BTEX exposure.

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)0.003
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 score0.094
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.370
Teacher spread0.315 · 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

Citations12
Published2014
Admission routes2
Has abstractyes

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