Network-Level Performance Measures for Low-Volume Highways in Alberta, Canada
Bibliographic record
Abstract
The province of Alberta is responsible for 30,740 km of highway, of which 4,320 km is gravel surfaced. Approximately 8,750 km (28.5%) carries traffic volumes of fewer than 400 vehicles per day. The objectives of this study are to outline network-level performance measures for highways in Alberta and to ensure that they are effective for managing low-volume highways. The performance measures used relate primarily to physical condition and functional adequacy. In 2002 these measures were studied as part of a joint project with the University of Calgary in an attempt to make them more budget sensitive. Criteria were determined and subsequently adopted by Alberta Infrastructure and Transportation. Subsequent to that, a study was undertaken to determine optimal levels for these measures. These optimal measures would be used to set desired future performance targets. The results showed that on the order of 5% of the highways should remain in poor condition and should not be part of the backlog to be corrected. It was at first thought that the majority of these highways would be low-volume roads, but this did not prove to be true, primarily because of the younger age of these roads. The study also examines the cost-effectiveness of projects to pave low-volume gravel roads in comparison with other projects in the program. Cost-effectiveness is defined as agency cost per user per kilometer over a 20-year life-cycle. In conclusion, a future direction is suggested for the department to follow with respect to its low-volume highway improvements.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".