Making Winter Driving Safer - Establishing Performance Standards for Winter Maintenance
Bibliographic record
Abstract
In a series of collaborative trial projects that started in the winter of 2013/14, Alberta Transportation and the province's highway maintenance contractors developed and tested performance standards for winter highway maintenance. Drivers will benefit from the introduction of performance standards for winter maintenance through anticipating, and experiencing, more consistent driving conditions during the winter. Performance standards will also allow consistent educational and public awareness messaging, which will in turn promote safer trip planning. Various types of performance measures are either in place or are being tested in Alberta, ranging from input measures (i.e. material stockpiling) through process measures (i.e. response times), output measures (i.e. time to return to specified conditions), surrogate outcome measures (i.e. surface friction) and true outcome measures (i.e. travel speeds). Alberta benefited from experience with established technologies like Automated Vehicle Location Systems, Traveler Information Systems, and precision forecasting/Maintenance Decision Support Systems when developing the trial performance standards. In addition, supporting management tools were developed as part of the performance standards trials. These include a Winter Severity Index that can be used on both provincial & local scales, and Storm Classification that allows the contractors to work towards different performance targets, depending on the severity of each storm event. The paper will describe performance standards that are under development for various phases of winter maintenance work planning and execution, and discuss some of the implications of using performance standards in a contracted delivery system. Results of the different trial projects are presented, with concluding remarks on the safety benefits from the introduction of formal performance standards.
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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 | 0.113 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".