Canadian Truck Size and Weight Policy Development: Are There Lessons for the U.S.?
Bibliographic record
Abstract
Canada has ten provinces and three territories, each with responsibility for truck size and weight regulations. These regulations became increasingly diverse by the mid 1970’s, and resulted in many vehicles with undesirable dynamic performance and/or excessive impact on infrastructure. The provinces determined that the diversity in regulations was a barrier to internal trade, and collectively, created a process that has now effectively harmonized them. This paper documents the history of changes made by the provinces through the 1970’s and 1980’s, the process used to harmonize them, and describes how the process continues to be used today to maintain the regulations. The paper identifies steps taken in the 1970’s and 1980’s that resulted in unexpected and undesirable outcomes, and steps taken during the harmonization process that resulted in the intended outcomes. These provide useful technical lessons, which may be of use to the U.S. federal government, a state, or a group of states, if any should choose to make changes to their truck size and weight regulations. The process used in Canada was administrative and non-political, and it had a well focused purpose of achieving size and weight harmonization to increased transport efficiency and national competitiveness. The subject matter contained was complied for a research project titled “Review of Canadian Experience with Large Commercial Motor Vehicles” sponsored by the National Cooperative Highway Research Program (NCHRP).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".