TRAP and traffic data for Oshawa and Allen Road
Bibliographic record
Abstract
This dataset includes original data used in the creation of the article titled “Localized Variabilities in Traffic-related Air Pollutant Concentrations Revealed Using Compact Sensor Networks”. Supporting reference data can also be found in the reference dataset “Reference PM2.5 and Wind Data for Oshawa and Allen Road” (doi: 10.17632/yw49w4d7v2.1). The aim of this research was to demonstrate the usefulness of real-time air quality monitoring in the context of Smart City infrastructure. Data collected before and during the COVID-19 pandemic in Oshawa, Ontario includes 2-minute averaged TRAP (CO, NO, and PM2.5) concentrations measured by AirSENCE and hourly traffic volume data measured by Northline Fox traffic counters. The data demonstrates a direct relationship between decreased traffic volumes and concentrations of TRAP. Conversely, road construction was correlated with higher levels of TRAP while causing reduced traffic volumes, demonstrating the insufficiency of conventional sensors for reliably inferring air quality conditions and the need for compact air quality sensor networks. Other data included in this dataset were collected in 2021–2022 on opposite sides of Allen Road, a busy commuter route in Toronto, Ontario. Data here include 10-minute averaged TRAP (CO and NO) concentrations measured by AirSENCE. This part of the study highlighted the importance of local meteorological conditions with respect to the dispersal of TRAP as well as the applicability of compact air quality sensor networks for supporting in-depth studies of TRAP emission sources and human exposure pathways.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".