A Semi-parametric Regression Model to Estimate Variability of NO2
Bibliographic record
Abstract
The purpose of this analysis was to derive a land-use regression (LUR) model using a semi-parametric method (based on penalized splines) to estimate the geographical characteristics that influence ambient concentrations of nitrogen dioxide (NO2) in Montreal, Quebec, Canada. Such estimations are often used to assess exposure to traffic-related pollution in epidemiologic studies. In May 2003, levels of NO2 were measured for 14 consecutive days at 67 sites across the city, using Ogawa passive-diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). This work is re-analyzing of these data. Linear and semi-parametric multivariate regression analyses were conducted to assess the dependency between logarithms of concentrations of NO2 and land-use variables. In the published multiple linear regression analyses for this study, distance from the nearest highway, length of highways and major roads within 100 m, traffic count on the nearest highway, and population density showed significant associations with NO2. The best-fitting linear model had a R2=0.54. The most important variable in the model was traffic count on the nearest highway. The next most important variable was distance from the nearest highway, which has a negative association with NO2 concentration. This work used a semi-parametric model with a nonparametric part incorporating the variables “area of open space within 100 m” and “length of minor roads within 500 m”. These variables were non-significant in the linear regression model and showed nonlinear associations with the level of NO2. The semi-parametric model improves the fit of the model for land-use regression when comparing observed and predicted results.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".