Land-use regression modelling of highway ultrafine particle number concentrations
Bibliographic record
Abstract
Ultrafine particles (UFP; diameter < 100 nm) are suspended atmospheric solids of concern to \nhuman health due to their small diameters. Vehicles emit significant numbers of UFP, leading to \nsteep spatial gradients on, and near, high traffic roadways. Particle number concentrations were \nmeasured while driving a cyclic route on four Toronto highways during non-rush hour weekday \nperiods during the summer. These data were averaged over roadway length segments to create a \nconcentration map of the route. Road segment-averaged concentrations were bimodal, and ranged \nfrom 26,000 to 106,500 particles cm-3, which is significantly greater than the average background \ndowntown Toronto concentration (15,500 cm-3). A land-use regression model incorporating diesel \ntruck traffic, six land use types, and distance to Lake Ontario, accounted for 34% of the variability \nof on-road concentrations. Understanding the complex spatial distribution of urban UFP is integral \nto designing epidemiological studies and understanding commuter pollutant exposure. / Des particules ultrafines (PUF; diamètre < 100 nm) sont des solides en suspension atmosphériques \nqui sont source de préoccupation pour la santé humaine en raison de leurs petits diamètres. Les \nvéhicules motorisés émettent un grand nombre de PUF, ce qui résulte dans la formation des gradients spatiaux prononcés sur, et à proximité, des autoroutes à haute densité. Ici, des concentrations \nnumériques des particules ont été mesurées en empruntant des routes en vélo au bord de quatre \nautoroutes situés à ou près de Toronto en dehors des heures de pointe en semaine pendant l'été. \nUne moyenne des données a été établie sur des segments de chaussée afin de créer une carte de \nconcentration sur ces itinéraires en particulier. La moyenne des concentrations sur des segments \nde chaussée a été bimodale, et variait de 26,000 à 106,500 particules cm-3, ce qui est nettement \nsupérieur à la concentration en moyenne des rues de référence situé dans le centre-ville de Toronto \n(15,500 cm-3). Un modèle de régression sur l’aménagement du territoire qui intégrait la circulation \ndes camions à moteur diésel, six genres d’aménagement du territoire, et la distance du lac Ontario, \nreprésentaient 34% de la variabilité des concentrations sur les autoroutes. La compréhension de la \ndistribution spatiale complexe des PUF urbaines est partie intégrante de la conception des études épidémiologiques et de la compréhension d’exposition aux polluants pour des navetteurs de banlieue.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".