Winter Performance Measures in Alberta, Canada
Bibliographic record
Abstract
Performance measurement is a vital component of asset management, which is used in planning and programming to identify assets that are under or over performing and to assess overall performance. As part of the move to asset management, Alberta Infrastructure and Transportation has implemented performance-based planning and monitoring of the provincial highway network. Furthermore, since Alberta is a winter province, a clear suite of performance measurement tools is required for snow and ice control. Traditionally agencies have measured inputs or outputs, but none of the existing measures address effectiveness. Standards are in place for times to correct pavement to a certain condition after a storm ends, yet monitoring of these standards is not done consistently across the province or summarized for others to see. This paper presents the results of a project to develop winter performance measures that are outcome based for a large rural highway network. This paper includes results of an extensive pilot project which was carried out in the winter of 2004-2005 on approximately 300 km of Highway 2 from Calgary to Edmonton. The pilot project evaluated the use of several factors for performance measure development. These measures included the good, fair, and poor ratings provided by maintenance contractors and reported for public use through the provincial motor association, collision and run-off-the-road incidents, and vehicle speed and volume distributions during storm events. Categorization of storm events was a further subject of study. The paper concludes with recommendations for further work for the winter of 2005-2006.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".