Taking the Politics Out of Paving: Achieving Transportation Asset Management Excellence Through OR
Bibliographic record
Abstract
The New Brunswick Department of Transportation (NBDoT) maintains over 18,000 kilometers of roads, 2,900 bridges, various ferry crossings, and other assets. Because of its limited budget, NBDoT faced significant challenges in rehabilitating its infrastructure assets valued at several billion dollars. Its goal was to develop transparent, defensible, long-term plans for managing New Brunswick's highway infrastructure, and secure commitment from decision makers and support from the public for these plans. The operations research component of the asset management framework uses a unique combination of linear programming and heuristic techniques. The model incorporates long-term objectives and constraints from an operations perspective—it weighs all options; considers costs, timings, and asset life cycles; and produces optimal treatment plans and schedules of activities. NBDoT anticipates $72 million (discounted) in annual savings, amounting to $1.4 billion (discounted) over the next 20 years. NBDoT has become a global leader in the field of asset management, and the success has attracted the attention of transportation officials around the world.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".