Probing sources of strontium exposure in pregnant individuals living near unconventional oil and gas wells using urinary 87Sr/86Sr isotope ratios
Bibliographic record
Abstract
Abstract Background In the Exposures in the Peace River Valley (EXPERIVA) study, pregnant individuals living in a region of natural gas exploitation had higher biological concentrations of certain trace elements, including strontium (Sr), than the general population. However, sources remained unidentified. Objectives To measure urinary 87 Sr/ 86 Sr isotope ratio in EXPERIVA participants, assess its reliability, and explore how its variance fluctuates based on Sr concentrations in biological (urine, hair, nails) and environmental (tap water) samples, as well as the density/proximity of unconventional oil and gas wells around participants’ residence. Methods Participants provided urine daily over seven consecutive days. We measured 87 Sr/ 86 Sr in each urine sample from 7 participants and in pooled daily samples for all 75 participants. We used serial measurements to determine the intraclass correlation coefficient (ICC). We calculated the density/proximity of unconventional oil and gas wells around participants’ homes using inverse distance weighting (IDW). We assessed the variance of urinary 87 Sr/ 86 Sr based on Sr concentrations in biological/environmental samples and IDW through visual inspection and Levene’s test. We also performed unsupervised clustering to explore whether certain characteristics of the participants may be associated with a specific 87 Sr/ 86 Sr signature. Results Urinary 87 Sr/ 86 Sr ranged from 0.70798 to 0.71437. The ICC was 0.797 (95% CI: 0.574–0.953), indicating moderate to excellent reliability. Increasing Sr concentrations in hair were marginally associated with a decrease in urinary 87 Sr/ 86 Sr variance ( p = 0.066). A similar but less consistent association was observed with increasing IDW. We observed no association between Sr concentrations in water and variance in urinary 87 Sr/ 86 Sr. No clear pattern was found using unsupervised clustering. Impact To our knowledge, this study is the first to explore the use of urinary 87 Sr/ 86 Sr isotope ratios to investigate sources of Sr exposure. Results are consistent with the hypothesis that a predominant source contributes to Sr exposure in most exposed EXPERIVA participants, but the contribution of unconventional oil and gas wells around participants’ residences remains unclear. Findings should be considered as exploratory given the many limitations of this study. Our effort will hopefully benefit future studies aimed at identifying the sources of exposure in human populations.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".