World-class winter services and management
Bibliographic record
Abstract
The Swedish Road Administration (now known as the Swedish Transport Administration) endeavored to benchmark winter services and management in order to improve the maintenance of winter services and pavements. Eight countries/states that include Norway, Sweden, Finland, Denmark, Scotland, Slovenia, Minnesota Department of Transportation (MnDOT) in USA, and Alberta, Canada were included in the survey. In Sweden, customer satisfaction surveys for winter services indicated lower than desirable results. Therefore, the objective of the international survey would allow comparison and determination of innovative maintenance and management practices from world class countries. This study would benchmark winter services, and outsourcing issues, and management practices in cold climate countries. This international survey used a comprehensive custom-made questionnaire approach, which was followed-up by country interviews in the participating cold climate countries. The study was carried out by a team from Aalto University in Finland and took place from October 2009 to June 2010. A final workshop was held to allow countries to present their best practices and engage in a brainstorming workshop to solicit challenges, innovative ideas, and possible continuation. It is important to understand how different countries approach management, quality practices and customer services to provide them, the road users, with satisfactory/reasonable road condition during the winter time. The results from the study could be used by all participating countries to change or re-engineer their own road management practices. The results indicate that winter services are very difficult to compare both nationally and internationally as weather, road type, different maintenance standards, different survey methods and questionnaires, outsourced or in-house maintenance, and different practices are all present. However, the results demonstrate that similar issues were encountered in the participating countries. Benchmarking other countries practices revealed that existing practices can be improved and in spite of differences, it can be fruitful to benchmark road management practices.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; both teacher heads agree on what is shown here.
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".