Estimation of exposure to particulate matter in pregnant individuals living in an area of unconventional oil and gas operations: Findings from the EXPERIVA study
Bibliographic record
Abstract
Northeastern British Columbia (Canada) is an area of oil and gas exploitation, which may result in release of fine (PM2.5) and inhalable (PM10) particulate matter. The aims of this study were to: 1) apply extrapolation methods to estimate exposure to PM2.5 and PM10 concentrations among EXPERIVA (Exposures in the Peace River Valley study) participants using air quality data archives; and 2) conduct exploratory analyses to investigate correlation between PM exposure and metrics of oil and gas wells density, proximity, and activity. Gestational exposure to PM2.5 and PM10 of the EXPERIVA participants (n = 85) was estimated by averaging the concentrations measured at the closest or three closest air monitoring stations during the pregnancy period. Drilling metrics were calculated based upon the density and proximity of conventional and unconventional oil and gas wells to each participant’s residence. Phase-specific metrics were determined for unconventional wells. The correlations (ρ) between exposure to PM2.5 and PM10 and metrics of well density/proximity were determined using Spearman’s rank correlation test. Estimated PM ambient air concentrations ranged between 4.73 to 12.13 µg/m3 for PM2.5 and 7.14 to 26.61 µg/m3 for PM10. Conventional wells metrics were more strongly correlated with PM10 estimations (ρ between 0.28 and 0.79). Unconventional wells metrics for all phases were positively correlated with PM2.5 estimations (ρ between 0.23 and 0.55). These results provide evidence of a correlation between density and proximity of oil and gas wells and estimated PM exposure in the EXPERIVA participants.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".