Winter Performance Measures in Alberta, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it