Emissions From Light-Duty Vehicles in Hamilton-Wentworth Under Policy Scenarios: Applications of an Integrated Model
Bibliographic record
Abstract
BackgroundThis paper reports on simulations conducted by the authors as members of the Transportation and Land-Use Workgroup of the Hamilton-Wentworth Air Quality Initiative (HAQI).HAQI is a co-operative project of the Regional Municipality of Hamilton-Wentworth, the Ontario Ministry of Environment and Energy, The Ontario Ministry of Transportation, Environment Canada, public interest groups, and a number of researchers from McMaster University.Its objective is to assess air quality problems and the factors that underlie them in the Hamilton metropolitan area.The mandate of the Transportation and Land-Use Workgroup is to assemble information on the impact of transportation on air quality, explore the link between evolving landuse patterns and pollution from transportation, and envision likely future trends in transportation emissions.As part of the committee's work, the authors conducted computer simulations of automobile emissions at ten-year intervals from 1991 to 2021 under a number of different policy scenarios.The scenarios, which were developed in consultation with the Workgroup, incorporate spatial projections of population, employment, household structure, and transportation infrastructure that were provided by the Region of Hamilton-Wentworth.They were executed using an integrated urban simulation model known as IMULATE (Integrated Model of Urban Land-use, Transportation, and Environmental analysis) that was developed by researchers at McMaster University and the University of Toronto!The pollutants for which emissions estimates are generated are
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".