Maternal Environmental Exposures and Birth Weight in Canada: Reducing Adverse Infant Health Outcomes
Bibliographic record
Abstract
This thesis examined the relationship between environmental exposures and adverse birth outcomes. The first study was a scoping review that investigated the associations between fine particulate matter (PM2.5), nitrogen dioxide (NO2), and greenness with birth weight (BW), low birth weight (LBW), and small-for-gestational-age (SGA) in Canada. Increased exposure to air pollutants was associated with decreased BW while the opposite was true for greenness. A slight association was found between increased PM2.5 exposure and LBW and SGA. Increased exposure to greenness was associated with decreased odds of SGA. The second study used logistic regression models to investigate the associations between PM2.5, NO2, and greenness with LBW. It was found that increased greenness was associated with decreased odds of LBW in infants from low-risk pregnancies in Ontario. The results of this thesis suggest that the maintenance of and access to green areas may lead to improved birth outcomes in Canada.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".