Performance Measures for Snow and Ice Control in Province of 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 over time. As part of the move to asset management, Alberta Transportation has implemented performance based planning and monitoring of the provincial highway network. Three performance measures, based upon technical measurement, have been adopted which characterize network condition, functional adequacy and utilization. However, Alberta, like the rest of Canada, is a winter province yet no clear suite of performance measure has been developed for snow and ice control. Traditionally, agencies have measured inputs (such as salt or sand) or outputs (such as plowing frequencies), but none of the existing measures address effectiveness. This paper presents the results of a project to develop winter performance measures that address both the planning and operations of a large rural highway network. Preliminary results indicate that traffic volumes and speed data can be used to identify major storm events and as such may hold promise as repeatable, robust, relevant and responsive performance measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| 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".