Climate Change and its Potential Impact on Winter-Road Maintenance: Temporal Trends in Hazardous Temperature Days in the United States and Canada
Bibliographic record
Abstract
This paper describes how climatic changes will likely bring warmer air temperature in coming years. Although mean air temperature usually is employed to represent the magnitude of climatic change, changes in mean values are difficult to translate into changes in winter-road maintenance. Two variables derived from minimum and maximum air temperatures are used here in order to evaluate changes in winter-road maintenance over North America. Hazard days are those days with minimum temperatures recorded in a near-freezing temperature range, while below-freezing days are those days with maximum temperature not reaching 0°C (32°F). Over North America, there are differential trends of hazard days across the continent while there is a general decrease in the numbers of below-freezing days over the period 1948-2002. The regions with increasing numbers of hazard days are generally where minimum-temperature trends are positive. The regions with positive trends of below-freezing days cluster in the Ohio valley region. As climatic change takes place and the spatial distribution of hazard and below-freezing days changes, the types and intensity of winter-road maintenance activities will change. Allocation of resources and personnel need to be evaluated accordingly. By analyzing the recent 55 winter seasons of air-temperature data for the U.S. and Canada, the spatial distribution and trends of variables relevant to winter-road maintenance are illustrated. The paper concludes by discussing a number of possible impacts of climate change on winter-road maintenance in the future.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".