ADVERSE PERINATAL OUTCOMES AND ENVIRONMENTAL HAZARDS USING A WATERSHED APPROACH
Bibliographic record
Abstract
Background and Aims: Environmental contaminants linked to increased risks of adverse perinatal outcomes are varied and numerous. The watershed approach framework is able to accommodate a multi-stressor environment as it focuses on hydrologically-defined geographic regions rather than on a single discharger or specific media (e.g. air, water). This paper examines the feasibility of using a watershed approach in the analysis of environmental contaminants and reproductive health in British Columbia, Canada. Methods: Point-source pollution data and adverse birth outcomes were mapped using two similar sized but vastly different spatial tessellations of local watershed areas and administrative census subdivision areas. Pollution data was modelled using the cumulative annual release of a substance within both spatial tessellations and visually compared. Similarly, risk ratios of small-for-gestational age, preterm births and congenital anomalies were calculated for both spatial tessellations and a sensitivity analysis performed to assess rate stability. Results: Unlike administrative census boundaries, watershed areas are independent of population size and therefore were more appropriate to model the environmental hazard data particularly for rural and remote areas with low population densities. With respect to birth outcomes, both tessellations were able to pick up many of the same community-level risk estimates thus confirming and often spatially refining the found result. Due to their slightly larger size, the watershed areas produced more stable risk ratios with less variability when sensitivity analyses were performed (70% vs. 50% of areas remaining significant after sensitivity analysis). Conclusions: The watershed defines an appropriate small-area unit in which to investigate the cumulative impact of multiple physical, chemical, and biological stressors on human populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".