Follow-Up Study of Winter Standard as a Research and Development Project
Bibliographic record
Abstract
Norway is divided into 103 winter maintenance contracts—first quarter on bid in 2003 and the last quarter on bid in 2006. One of the consequences of the competitive tendering is a decrease in the research and development (R&D) projects involving the contractors. This is why the Norwegian Public Roads Administration has introduced challenging measures to maintain a required level of research in winter maintenance. In one of the contracts starting in autumn 2006, there has been set aside US$100,000 per year in the 7-year contract period to stimulate research projects. This amount of money is meant to be used for investment in equipment and to cover extra costs for the contractor and road keeper. The project consists of a main project with focus on a follow-up study of the winter standard on the most important road in the contract area. Two other subprojects planned for are (a) sanding under difficult conditions with different gradation of the sand to investigate the importance of the grain size on the friction improvement and duration of a sanding action and (b) the relationship between pavement condition and amount of winter maintenance actions. This paper describes the background and content of the main project and the two subprojects and also some preliminary results. The results from the project so far are positive from both a professional and an organizational point of view. R&D within the area of winter maintenance is already included in several of the contracts renewed in 2007.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".