Exposure to traffic related air pollutants: self reported traffic intensity versus GIS modelled exposure
Bibliographic record
Abstract
BACKGROUND: In epidemiological studies of the potential health effects of traffic related air pollution, self reported traffic intensity is a commonly used, but rarely validated, exposure variable. METHODS: As part of a study on the impact of Traffic Related Air Pollution on Childhood Asthma (TRAPCA), data from 2633 and 673 infants from the Dutch and the German-Munich cohorts, respectively, were available. Parents subjectively assessed traffic intensity at the home address. Objective exposures were estimated by a combination of spatial air pollution measurements and geographic information system (GIS) based modelling using an identical method for both cohorts. RESULTS: The agreement rates between self reported and GIS modelled exposure--accumulated over the three strata of self assessed traffic intensity--were 55-58% for PM(2.5), filter absorbance (PM(2.5) abs), and nitrogen dioxide in Munich and 39-40% in the Netherlands. Of the self reported low traffic exposed group, 71-73% in Munich and 45-47% in the Netherlands had low modelled exposure to these three air pollutants. Of the self assessed high exposed subgroups in Munich (15% of the total population) and the Netherlands (22% of the total population), only 22-33% and 30-32% respectively had high modelled exposure to the three air pollutants. The subjective assessments tend to overestimate the modelled estimates for PM(2.5) and NO2 in both study areas. When analysis was restricted to the portion of the Dutch cohort living in non-urban areas, the agreement rates were even lower. CONCLUSIONS: Self reported and modelled assessment of exposure to air pollutants are only weakly associated.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".