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Record W4406795674 · doi:10.1080/22423982.2025.2457786

Polycyclic aromatic hydrocarbons from environmental tobacco smoke and wood stoves dominate in settled house dust from Northwestern Ontario First Nations communities

2025· article· en· W4406795674 on OpenAlexaffabout
David McMullin, Anna K. Kirkland, Imdadullah Hidayat-ur-Rehman, Thomas Kovesi, Gary Mallach, J. David Miller

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsChildren's Hospital of Eastern OntarioHealth CanadaUniversity of OttawaCarleton University
Fundersnot available
KeywordsStoveSmokeTobacco smokeEnvironmental scienceEnvironmental protectionEnvironmental healthGeographyArchaeologyMeteorologyMedicine

Abstract

fetched live from OpenAlex

Rates of respiratory tract infections for children living in remote First Nations communities in the Sioux Lookout Zone in Northwestern Ontario are elevated and associated with poor indoor environmental quality including high exposures to endotoxin and serious dampness and mould damage. The studies also revealed a high prevalence of cigarette smoking and most houses have wood stoves, of variable quality. Depending on structure, polycyclic aromatic hydrocarbons (PAH) are carcinogens, immunotoxins and/or inflammatory mediators that are byproducts of the incomplete combustion of organic materials. Indoor sources of PAHs include tobacco smoke, cooking, and burning wood and/or fossil fuels for house heating. Twelve PAHs were measured in the <300 µm fraction of settled house dust by GC-MS in 59 houses. Nine PAHs were detected in all 59 houses, and median concentrations of individual PAHs measured ranged from 66 to 804 ng/g. PAHs associated with environmental tobacco smoke and with wood smoke dominated the PAH profile. Limiting tobacco smoking indoors and upgrading to low emission airtight wood stoves would improve indoor air quality and the respiratory health of children in this remote region of Ontario.

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 score0.385
Threshold uncertainty score0.675

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.294
Teacher spread0.274 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

Explore more

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