Density and proximity of unconventional oil and gas wells and concentrations of trace elements in urine, hair, nails and tap water samples from pregnant women living in Northeastern British Columbia
Bibliographic record
Abstract
The Peace River Valley (British Columbia, Canada) is an area of intensive unconventional oil and gas (UOG) exploitation, an activity that can release contaminants with possible adverse effects on the fetus. My project aimed to estimate the importance of this exposure. For this aim, we 1) measured concentrations of 21 trace elements in tap water and biological (hair, urine, nails) samples from 85 pregnant women in this region; 2) compared them with those from the general population and health-based guidance values; 3) assessed their correlations between matrices; and 4) evaluated their associations with the density and proximity of UOG wells (i.e., wells within radii of 2.5 km, 5 km, 10 km, and with all wells in British Columbia around residences). Spearman's rank correlation and multiple linear regression analyses adjusted covariates were performed. Our results showed higher urinary and hair levels of certain trace elements compared to reference populations (e.g., Co, Ba, Sr, Mn, V, Ga). Concentrations in tap water correlated strongest with concentrations in hair, followed by nails and urine. Positive (e.g., Al, Mn, Cu, Ga, Cd, Ba, Cr, Sr, U) and negative (e.g., Fe) associations were observed between the density and proximity of UOG wells and the concentrations of certain trace elements in tap water, hair, and nails. Our results suggest that pregnant women living in an active area of UOG exploitation are likely to be more exposed to certain trace elements than the general population, but the association with density and proximity to wells remains uncertain.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".