A Land Use Regression Model for Ultrafine Particles in Vancouver, Canada
Bibliographic record
Abstract
Methods to characterize chronic exposure to ultrafine particles (UFP) can help to clarify potential health effects. Since UFP are not routinely monitored in North America, spatiotemporal models are one potential exposure assessment methodology. Portable condensation particle counters were used to measure particle number concentrations (PNC) to develop a land use regression (LUR) model. PNC, wind speed and direction were measured for sixty minutes at eighty locations during a two-week sampling campaign. We conducted continuous monitoring at four additional locations to assess temporal variation. LUR modeling utilized 135 potential geographic predictors including: road length, vehicle density, restaurant density, population density, land use and others. A novel approach incorporated meteorological data through wind roses as alternates to traditional circular buffers. The range of measured (sixty-minute median) PNC across locations varied seventy-fold (1500-105000 particles/cm(3), mean [SD] = 18200 [15900] particles/cm(3)). Correlations between PNC and concurrently measured two-week average NOX concentrations were 0.6-0.7. A PNC LUR model (R(2) = 0.48, leave-one-out cross validation R(2) = 0.32) including truck route length within 50 m, restaurant density within 200 m, and ln-distance to the port represents the first UFP LUR model in North America. Models incorporating wind roses did not explain more variability in measured PNC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".