Estimation of the Relationships of Flexible Pavement Deterioration to Traffic and Weather in Canada
Bibliographic record
Abstract
In the Canadian context, climatic factors are a major cause of pavement deterioration. Temperature, frost and thaw action as well as moisture are factors that can cause certain types of pavement deterioration. These factors can also intensify pavement deterioration caused by heavy vehicles. Good estimates of the proportion of damage that can be attributed to climatic factors relative to those caused by heavy vehicles is required to conduct cost allocation studies. This paper describes a study done in Canada in order to assess damage ratios for different classes of road and environmental conditions prevailing in the country. Several studies were first reviewed and synthesized to obtain a preliminary set of ratios and to document methodologies used. A study was then conducted based on existing test sections available across the country. The actual condition of the section was used to compute a condition index including the combined effect of climate and traffic. Climatic effects were then removed using mechanistic and empirical models calibrated to the condition of each section. The site condition calculated using this procedure was then used to compute a new condition index representing the effect of traffic alone. The result of the study is a table of traffic/climate damage ratios developed for various conditions across the Canadian road network.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".