Safety performance of longer combination vehicles relative to other articulated trucks
Bibliographic record
Abstract
This article helps improve the understanding about the safety performance of longer combination vehicles (LCVs) relative to other articulated trucks operating on rural highways, using evidence from the Canadian portion of the CANAMEX trade corridor. The analysis reveals that from a collision rate perspective, LCVs as a group have better safety performance than other articulated trucks. Turnpike doubles have the lowest collision rate of all articulated truck types (16 collisions per 100 million vehicle-kilometres of travel or VKT), followed by Rocky Mountain doubles (32 collisions per 100 million VKT). The collision rate for triple trailer combinations (62 collisions per 100 million VKT) is higher than the collision rates for tractor semitrailers (42 collisions per 100 million VKT) and legal-length tractor double trailers (44 collisions per 100 million VKT). These results are an important input for civil engineering and transport policy decisions concerning longer combination vehicle operations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".