Spatial Modelling of Preterm Birth Near the Sydney Tar Ponds, Nova Scotia, Canada
Bibliographic record
Abstract
The major objective of the research is to assess the risk of preterm birth associated with maternal proximity to hazardous waste and pollution from the Sydney Tar Pond sites in Nova Scotia, Canada. The design is spatial modelling of risks of preterm birth in population living in the Cape Breton regional municipality in 1996. The subjects are: 1604 observed cases of preterm birth out of total population of 17559 at risk in 1996. The analysis was done using both the frequentist and the Bayesian approaches. In the frequentist approach, the Poisson model for aggregated data was fitted using the quasi-likelihood approach to accommodate over-dispersion. Weighted regression was also used. In order to accommodate both the random effect and the anticipated spatial effects, Bayesian hierarchical modelling was also used to fit the Poisson model. The result of the Bayesian modelling shows that there is no significant spatial association of risk in the area studied. All the models also show that there is no decrease in risk of preterm birth as we move from the Tar Pond site to other region. None of the other covariates in the model show any significant association with increase risk of preterm birth either. There was no obvious clustering of risk in any region or part.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".