Development of A Winter Severity Index for Salt Management in Canada
Bibliographic record
Abstract
This paper discusses the development of two winter severity indicator models that can be used to evaluate the relative harshness of a winter in comparison with a base period. A winter severity index is a measure of the relative impact of winter weather on winter road maintenance operations using historical meteorological or road weather information system data. Winter road maintenance data were collected from across. Salt usage in tons (salt (t)/lane-km/day) was chosen as the dependent variable, standardized to account for differences in road network and the number of days in the observation period. The first model developed based on meteorological data alone achieved a goodness of fit of 0.54. Explanatory variables were based on snowfall occurrence, air temperature, freezing rain occurrence, and an east-west dummy variable. A second model was developed based on meteorological data together with road weather information system data. This achieved a goodness of fit of 0.60, but was based on a significantly smaller sample size. In this model, pavement temperature was substituted for air temperature. An Index was developed based on the predicted values using a scale between 1 and 100. Calibration factors were developed for twenty different homogeneous groupings across Canada using the Bayesian method. Based on the calibration, thirteen of the twenty groups achieved a better goodness of fit compared to the national model results. The model results show a better performance in heavily populated areas and in eastern Canada. Limitations of the models and recommendations for further research are presented in the paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".