MOESM1 of Road proximity, air pollution, noise, green space and neurologic disease incidence: a population-based cohort study
Bibliographic record
Abstract
Additional file 1: Table S1. Correlations between road proximity, air pollution, noise and greenness in cohorts of Non-Alzheimer’s dementia, Parkinson’s disease, Alzheimer’s disease and Multiple sclerosis. Table S2. Distributions of exposures from Canadian Urban Environmental Health Research Consortium (CANUE). Table S3. Hazard ratios between exposures and Non-Alzheimer’s dementia stratified by sex (Males, Females), ethnicity (Chinese, South Asian, Visible Minority) and age (> = 65 years, < 65 years). Table S4. Hazard ratios between exposures and Parkinson’s disease stratified by sex (Males, Females), ethnicity (Chinese, South Asian, Visible Minority) and age (> = 65 years, < 65 years). Table S5. Odds ratios between exposures and Alzheimer’s disease and Multiple sclerosis stratified by ethnicity (Chinese, South Asian, Visible Minority). Figure S1. Hazard ratios (95% confidence interval) associated with road proximity for non-Alzheimer’s disease and Parkinson’s disease. HW = Highway, MR = Major road. Figure S2. Odds ratios (95% confidence interval) associated with road proximity for Alzheimer’s disease and Multiple sclerosis. HW = Highway, MR = Major road. Figure S3. Odds ratios (95% confidence interval) associated with air pollution, noise and greenness (per Interquartile range as indicated in Table 3) for Alzheimer’s disease and Multiple sclerosis. PM2.5 = fine particulate matter, NO2 = Nitrogen dioxide, NO = Nitric oxide.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".