Classification Algorithm for Characterizing Long Multiple Trailer Truck Movements
Bibliographic record
Abstract
Long multiple trailer trucks consist of a tractor and two or three semitrailers or trailers that exceed the maximum basic length limitation of 25 meters (82 feet) specified by provincial truck size regulatory schemes in Canada. Over time, the extent and nature of long truck operations have changed due to modifications in the region’s highway network, its regulatory environment, and the growing demand to improve regional economic competitiveness. Highway agencies face increasing pressures to permit long truck operations, but currently have limited information to represent or characterize these movements. Understanding the current extent and nature of long truck operations in the Canadian Prairie Region freight transportation system is critical for road design and maintenance, intermodal freight planning, safety analysis, environmental assessment, financing highway infrastructure, economic evaluation, and truck regulation. An algorithm to isolate and classify long multiple trailer trucks is developed. The algorithm utilizes weigh-in-motion data obtained from stations situated on the region’s long truck network. The resulting long truck dataset provides the basis for characterizing the volume and weight of long multiple trailer truck movements. The research outcomes service the demand for an understanding of the volume and weight of long truck activity, and provide an analytical basis for forecasting changes in their activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".