Performance Measures for Pavement Assets under Performance Based Contracts
Bibliographic record
Abstract
Over the past decade, there has been a movement in North America towards Performance Based Contracts (PBC). In PBCs, the client agency specifies defined minimum performance measures to be met or exceeded during the contract period. PBC operates through a continuing performance measurement and review systems against a set of minimum level of services (LOS). Therefore, performance measures in contract administration are fundamental to the successful usage of this type of contract. The paper presents a review of PBC focusing on performance measures. A review of the current state-of-the-practice is conducted to identify key performance measures employed by various agencies. In addition, a literature review of several road agencies in North America is conducted to evaluate the important physical attributes agencies are using as performance inputs to evaluate the overall condition of the road assets. Moreover, the study provides a review of performance specifications implemented by the Ministry of Transportation in Ontario (MTO) including Pavement with Warranty and Minimum Oversight Contracts. A monitoring framework of performance measures is presented. Finally, recommended performance measures for flexible, rigid pavements and granular shoulders are presented for the use in MTO's PBCs.
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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 | 0.030 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".