Effects of residential exposure to steel mills and coking works on birth weight and preterm births among residents of Sydney, Nova Scotia
Bibliographic record
Abstract
The Sydney Tar Ponds in Sydney, Nova Scotia, Canada, have been referred to as the most contaminated industrial site in the country. Area residents, aware of the contamination for close to 25 years, are very concerned about their health: past, present and future. In particular, they are concerned about cancer; this is followed closely by reproductive health concerns. The objective of this study was to assess the prevalence and determinants of birth weight and preterm birth among residents living in proximity to an industrial site contaminated with a mix of industrial wastes arising from a former steel mill and coking works (the Tar Ponds) in Sydney, Nova Scotia, Canada. A telephone survey was administered to 500 randomly selected women living in proximity to the site to document reproductive histories. The respondents reported 904 live birth pregnancies occurring in Sydney. The mean birth weight reported for live birth pregnancies was 3,391 g (standard deviation: 570). The reported prevalence of preterm births was 7 percent. Linear regression analyses showed a reduction in birth weight of approximately 85 g among residents in a zone estimated to have higher levels of deposition of airborne effluent from the site. Multivariate logistic regression analyses showed a decrease in the risk of preterm birth as residential distance from the site increased (odds ratio 0.65, 95 percent confidence interval 0.42, 0.98). These data suggest that community exposure to industrial contaminants had some, but little, impact on reported birth weights in this sample of respondents. The apparent increase in risk for preterm births suggests further investigation may be warranted, particularly in light of community concerns. Follow‐up investigations using validated birth records and more sensitive measures of environmental exposure could be used to confirm the findings and further explore the intra‐urban variation in health outcomes. These findings underscore the importance of ongoing clean‐up efforts in Sydney.
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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 | 0.000 | 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 | 0.000 |
| 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".