AN EVALUATION OF VARIOUS PRIORITIZATION METHODS FOR EFFECTIVE PAVEMENT MANAGEMENT: A CANADIAN AIRPORT CASE STUDY By:
Bibliographic record
Abstract
Tighe et al. 1 Proper Funding for current and future needs has always been a problem for the management of pavements. With the introduction of prioritization it has enabled engineers and managers to identify those pavement sections that need attention. Prioritization of current needs plays an important role in the management system. In short, Prioritization becomes an effective tool for supporting decisions to be taken for effective pavement management. The management system strives to achieve the maximum benefits through prioritization. Depending on the funding levels, location, and specific conditions of a transportation agency, different methods ranging from a simple subjective ranking of projects based on judgment to comprehensive optimization by mathematical programming models, are being used for determining priorities. This paper presents a case study for airports, where the Pavement Condition Index (PCI), which has been recognized as an ASTM Standard Test Method for Airfields, has been calculated by entering the distress data of 271 sections in MicroPAVER. Current needs have been
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".