Pavement-effects on heavy-vehicle fuel consumption in cold climate using a statistical approach
Bibliographic record
Abstract
Policy-makers are investigating ways to reduce greenhouse gas emissions of the transportation sector. Many government agencies have studied the effects of pavement type and characteristics on fuel consumption. Such models, valid for a particular set of simulation conditions, suggest that rigid pavements perform better than flexible pavements. However, it is unclear how this type of model can be applied at the scale of a realistic road network. This paper proposes a statistical approach to assess the relative importance of pavement type and characteristics on fuel consumption of heavy vehicle traffic by considering the specificities of meteorological conditions, heavy vehicles, and driver behaviours. A case study in Canada showed that the potential advantage of using rigid pavements was offset by cold climate effects and the consideration of a realistic statistical payload distribution. Road roughness accounted on average for 1.1% of total fuel consumption, with an increased value of 1.9% in January.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".