An Exploration of TRAP Exposure and Patterns of Environmental Inequality at a High Spatial Resolution in Etobicoke-York, Ontario
Bibliographic record
Abstract
This thesis addresses two objectives. The first objective explores the use of high spatial density urban sampling and regression kriging to improve land use regression (LUR) modelling performance for predicting ambient nitrogen dioxide (NO2) at a high spatial resolution across Etobicoke-York, Ontario. The second objective explores marginalization as a potential mechanism for disparate NO2 exposure in Etobicoke-York. This objective was met by using ordinary least squares (OLS) regression and simultaneous autoregressive (SAR) modelling techniques to identify spatial associations between NO2 exposure and fine-scale metrics of marginalization and by computing odds ratios (ORs) to capture the effect of marginalization on the odds of high versus low NO2 exposure levels. This thesis highlights improvements in exposure modelling performance for the incorporation of high spatial density monitoring data and regression kriging, as well as identifies significant patterns of disparate NO2 exposure in Etobicoke-York related to ethnic concentration, material deprivation, and residential instability.
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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 | 0.001 |
| 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 | 0.001 |
| 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".