Bibliographic record
Abstract
Saskatchewan Department of Highways and Transportation has been a leader in road and short-rail transportation initiatives that facilitate efficient commercial transportation to remote northern and rural areas. A recent initiative undertaken by Saskatchewan Department of Highways and Transportation is a public-private partnership with commercial carriers to allow larger truck configurations to haul primary weights on select secondary rural roads if the carrier deploys road friendly vehicle technologies, such as air-spring suspension and central tire inflation. The pilot partnership haul program has resulted in haul cost savings of between 20 and 50 percent, generating approximately four million dollars per year in total, which is subsequently shared between the carrier and Saskatchewan Department of Highways and Transportation. At the present time, equitable distribution of partnership haul cost savings and compliance audits are determined manually based on submitted logbooks and waybills. Because this manual system is labor intensive and prone to accounting errors, the administration of the partnership haul program is only tenable for larger carriers with semi-automated administration systems. As a result, a system that automatically allocates partnership haul cost savings and performs compliance audits is the enabling technology to expand the partnership program to small and mid-sized carriers. International Road Dynamics Inc. proposed and developed an automated vehicle monitoring and audit system that is designed to facilitate the Saskatchewan partnership program. This paper discusses the Saskatchewan partnership program and describes the technological features and advantages of the automated vehicle monitoring and audit system. This paper also discusses other potential applications of the automated vehicle monitoring and audit system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.875 | 0.784 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".