Development of Canadian asphalt pavement deterioration models to benchmark performance
Bibliographic record
Abstract
The Canadian Long Term Pavement Performance (C-LTPP) study, initiated in 1989, involves 65 sections located at 24 sites constructed with various asphalt overlay rehabilitation treatments. This study investigates the impacts of the various alternative rehabilitation treatments on pavement roughness progression. A series of models are developed for predicting the rate of pavement deterioration occurring for the first 8 years of service. The models examine both within-site factors and between-site factors. Site location is found to be the primary influence on the rate of pavement deterioration. Overlay thickness and the amount of cracking prior to rehabilitation are also determined to influence pavement deterioration at a strong statistical level. Models are provided for benchmarking the performance of pavements across Canada, for comparison with individual project designs, and for estimating the performance of designs with different overlay thickness.Key words: Canadian Long Term Pavement Performance program, roughness, pavement deterioration, site effects, asphalt overlays, benchmark, univariate analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".