Implementation of a Framework for Pavement Asset Preservation Programming in New Brunswick
Bibliographic record
Abstract
The New Brunswick Department of Transportation (NBDoT) is implementing a progressive and pragmatic asset management framework to provide a more strategic approach to long term, investment planning and program management for its entire transportation infrastructure. Pavements comprise a substantial portion of this asset base in terms of value and annual rehabilitation funding, and therefore warrant extra focus. The framework for pavement preservation is built upon a strategic, tactical and operational approach to long term management of pavement assets utilizing linear programming and program development. Performance modelling is performed at the strategic level to develop 20 year optimized pavement asset investment plans to support both tactical (i.e. short term forward-works programs) and operational (i.e. annual programs) planning where rehabilitation options can be assessed and prioritized. While the province had an established pavement monitoring program in place, applying it within the context of the asset management framework for long term pavement preservation was new. This paper focuses on the development of the initial suite of strategic pavement deterioration curves and operational windows to be used for estimating network level pavement rehabilitation. Key challenges faced during the implementation and areas identified for future efforts are also discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".