UTILISATION OF FIVE- AND SIX-AXLE TRACTOR SEMITRAILERS IN WESTERN CANADA.
Bibliographic record
Abstract
Analysis of trends in truck fleet mix and the relative productivity and operational characteristics of the five– and six–axle tractor–semi–trailers (342 and 3–S3) are presented. Marked increases in the use of 3–S3 at the expense of the 3–S2 are observed. The percentage of 342 in the heavy truck fleet dropped from about 70% in 1991 to about 50% in 1994, while the percentage of 343 increased from 9% and 20% over the same period. These changes could be explained by: better operating efficiency measured by the potential pavement damage per unit payload; flexible payload handling capability; and higher productivity indicated by the potential payload capacity actually utilised. Current trends in fleet mix changes suggest that the rate of increase in the percentage of 343 in the truck fleet is likely to be maintained in the next few years. Possible implications for trucks operations under North American Free Trade Agreement is that the 3–S3 offers a clear productivity advantage over the 3–52 and therefore suitable for long haul 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".