Planning for Winter Road Maintenance in the Context of Climate Change
Bibliographic record
Abstract
Abstract Winter weather creates mobility challenges for most northern jurisdictions, leading to significant expenditures on winter road maintenance (WRM) activities. While the science and practice of snow and ice control is continually evolving, climate change presents particular challenges for the strategic planning of WRM. The purpose of this study is 1) to develop a winter severity index (WSI) to better understand how winter weather translates into interannual variations in WRM activities and 2) to apply the WSI to future climate change projections to assist a northern community in preparing for climate change. A new method for creating a WSI model is explored, using readily available data from maintenance records and meteorological stations. The WSI is created by optimizing values for three levels of snowfall as well as potential icing events and is shown to have high predictive accuracy for WRM (coefficient of determination R2 of 0.93). The WSI is then applied to historic and future climate data in a municipality located in central British Columbia, Canada. Findings reveal that much of the variability in WRM can be attributed to weather. The results of the climate change analysis show that winter precipitation in the region is expected to increase by 5.2%–12.3%, and winter average temperatures are projected to increase by 1.5°–2.8°C in the 2050s, compared to the 1976–2000 baseline based on 65 GCMs. Based on the midrange (25th to 75th percentiles) of the 65 GCM projections, annual demand for WRM activities is estimated to decrease by 13.0%–22.0%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".