Issues and Options for Oversize/Overweight Permitting of Petroleum-Related Trucks in a Performance-Based Regulatory Context: The Manitoba Experience
Bibliographic record
Abstract
This paper presents a case study of the Manitoba experience in permitting petroleum-related oversize/overweight (OS/OW) truck traffic. In recent years, Southwest Manitoba, along with many regions throughout North America, has experienced rapid growth and change in the petroleum industry. This growth has fuelled economic development and also caused infrastructure challenges on rural roads that are being used by unique vehicle configurations, many of which are beyond basic truck size and weight (TSW) limits. Manitoba's OS/OW permitting program for these vehicles stems from the performance-based approach to TSW regulation being used in Canada since 1988 and relies on ongoing collaboration with the petroleum industry. Manitoba's experiences have led to several insights, which may be options for other jurisdictions facing similar issues related to OS/OW petroleum-related trucking. These insights include: (1) purposeful collaboration with the industry and officials in neighbouring jurisdictions to understand permitting needs and barriers; (2) supplementing qualitative understanding of the industry with quantitative data; and, (3) identifying opportunities to expedite permitting procedures by issuing annual permits to routinely-configured vehicles, utilizing technologies to assist with TSW enforcement, and rationalizing permit fee structures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".