Assessing traffic vulnerability to climate hazards in cold regions: the impact of harsh winters conditions on highway traffic volumes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The winter traffic models use five years of weigh-in-motion (WIM) data from the commuter roadway. This study used a model to calculate the percentage drop for each vehicle type, such as total, passenger cars, and truck traffic, based on 266 weather combinations consisting of seven cold categories and varied snowfalls. The developed models evaluate the marginal impact and combined effect of meteorological conditions on the proportional decrease in winter traffic volume. The predicted percentage decrease in traffic for all three vehicle classes rises as temperature and snowfall worsen. A mathematical formula was proposed to forecast the decreasing percentage trends. Roadway authorities may utilize the findings in the research to determine when to execute snowplowing operations for winter highway maintenance.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it