Analyse de l’association entre la densité/proximité de puits d’exploitation de pétrole et de gaz naturel et les concentrations de radon à l’intérieur des résidences du Nord-Est de la Colombie-Britannique
Bibliographic record
Abstract
Northeastern British Columbia (Canada) is a region of oil and gas exploitation. Oil and gas extraction activities can emit contaminants, including radon, but studies on indoor air contaminants in regions of oil and gas exploitation are scarce. This study aimed to evaluate the association between the density/proximity of oil and gas wells and indoor air radon concentrations in Northeastern British Columbia. Data from the British Columbia Radon Data Repository (BCRDR) and the Exposures in the Peace River Valley study (EXPERIVA) were used, with 497 radon measurements taken from dwellings between 1992 and 2019. Within different buffer zones around each dwelling (2.5, 5 and 10 km), well density was calculated and an exposure metric, Inverse Distance Weighting (IDW), of both well density and proximity was derived. Linear regression models were used to evaluate the associations between well density and IDW and indoor air radon concentrations while adjusting for the floor where measurement was taken. A higher radon concentration was measured in basements than upper floors. Statistically significant and negative associations (p<0.05) were observed between well density/proximity and radon concentrations. For example, an increase of one well within 10 km was associated with a modest decrease of 0.1% (95% CI: 0.05; 0.20) in radon concentrations. We observed no positive association between well density/proximity measurements and radon concentrations. The negative associations were primarily influenced by radon measurements made in 1992, when oil and gas development used very little hydraulic fracturing. Additional studies with more measurements, information on dwelling type, and phase of oil and gas operations could allow a more precise and powerful analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".