Density and proximity of oil and gas wells and concentrations of trace elements in urine, hair, nails and tap water samples from pregnant individuals living in Northeastern British Columbia
Bibliographic record
Abstract
BACKGROUND: Oil and gas exploitation can release several contaminants in the environment, including trace elements, with potentially deleterious effects on exposed pregnant individuals and their developing fetus. Currently, there is limited data on pregnant individuals' exposure to contaminants associated with oil and gas activity. OBJECTIVES: We aimed to 1)measure concentrations of trace elements in biological and tap water samples collected from pregnant individuals participating in the EXPERIVA study; 2)compare with reference populations and health-based guidance values; 3)assess correlations across matrices; and 4)evaluate associations with the density/proximity of oil and gas wells. METHODS: We collected tap water, hair, nails, and repeated urine samples from 85pregnant individuals, and measured concentrations of 21trace elements. We calculated oil and gas well density/proximity (Inverse Distance Weighting [IDW]) for 4buffer sizes (2.5 km, 5 km, 10 km, no buffer). We performed Spearman's rank correlation analyses to assess the correlations across elements and matrices. We used multiple linear regression models to evaluate the associations between IDWs and concentrations. RESULTS: Some study participants had urinary trace element concentrations exceeding the 95th percentile of reference values; 75% of participants for V, 29% for Co, 22% for Ba, and 20% for Mn. For a given trace element, correlation coefficients ranged from -0.23 to 0.65 across matrices; correlations with tap water concentrations were strongest for hair, followed by nails, and urine. Positive (e.g., Cu, Cr, Sr, U, Ga, Ba, Al, Cd) and negative (e.g., Fe) associations were observed between IDW metrics and the concentrations of certain trace elements in water, hair, and nails. SIGNIFICANCE: Our results suggest that pregnant individuals living in an area of oil and gas activity may be more exposed to certain trace elements (e.g., Mn, Sr, Co, Ba) than the general population. Association with density/proximity of wells remains unclear.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".