8th International Conference on Managing Pavement Assets KEY PAVEMENT PERFORMANCE INDICATORS AND PREDICTION MODELS APPLIED IN A CANADIAN PMS
Bibliographic record
Abstract
One of the key functions required for a pavement management system (PMS) is its ability to evaluate and predict future pavement conditions within an analysis period. This evaluation involves the use of pavement performance indicators and performance prediction models. While performance indicators deal with evaluation of pavement structural and functional performance from different perspectives of road serviceability level, performance prediction models are used to relate future pavement conditions with its current condition in conjunction with a number of influential factors such as age, traffic, and pavement structural and environmental conditions. This paper presents the performance indicators and their prediction models used in the Ministry of Transportation of Ontario’s PMS (MTO PMS). This paper covers the main issues related to pavement evaluation indicators and pavement condition prediction models, with the emphasis on examination of the general properties of the individual indicators such as sensitivity, rationality, economy and practicality. A list of pavement condition evaluation indicators used in MTO PMS for measuring and predicting pavement performance are analyzed by road functional class, pavement structure and surface type. The
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".