Bibliographic record
Abstract
While passenger car mobility and related tail-pipe emissions within urban areas have received considerable attention, very little is known about the contribution of commercial vehicles to mobile source emissions. More specifically, it is desirable to investigate not only the total volume of emissions, but also emissions by link in the transportation network. In this paper a methodology is discussed that enables the estimation of commercial truck emissions of nitrogen oxides (NOx), non-methane hydrocarbons (NMHC) or simply hydrocarbons (HC), carbon monoxide (CO), and particulate matter (PM) at the aggregate and link levels. This methodology has been applied to the Census Metropolitan Area (CMA) of Hamilton, Ontario, Canada. A truck origin-destination (O-D) matrix was supplied by the City of Hamilton. The concept of Passenger Car Equivalence (PCE) was used to transform this matrix into a passenger car O-D matrix. The integrated land-use and transport model IMULATE has been modified to incorporate the transformed commercial vehicle matrix, along with the matrix of passenger cars. The relative contribution of trucks to mobile source emissions during the morning peak period can be shown at the link level, or aggregated to the regional level (Table 1.0). A PCE value of zero implies the total absence of trucks in the network. Reported emission values in this case are attributed to passenger cars alone. PCE values greater than zero indicate the number of vehicles displaced in traffic flow by the presence of a single truck. Reported emissions under such conditions are affected by the presence of trucks. Table 1.0: Aggregate emissions and trips made for varying PCE values
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.759 | 0.633 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".