Taking the Politics Out of Paving: Achieving Transportation Asset Management Excellence Through OR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it