Asset Management Strategy for Unsealed Low-Volume Roads in New Zealand
Bibliographic record
Abstract
The majority of rural local roads in New Zealand are unsealed low-volume roads that require regular grading, and a contractor has developed an asset management strategy for unsealed roads. Key facets of this strategy included establishment of a companywide asset management team responsible for implementing the strategy, creation of a new senior management position dedicated to delivering this strategy, and integration of asset management principles and processes into normal business. This unsealed roads maintenance strategy identified the need for a low-cost, effective tool for roughness monitoring that quantitatively reflects the unsealed network condition and is not based on subjective perception, which is the current situation. After a review of all existing roughness measurement systems from around the world, the Opti-Grade system from Canada was acquired and is being used on unsealed road networks throughout New Zealand. An example of the benefits of outsourcing road maintenance to private contractors is the fact that it was the contractor that identified the need for a low-cost roughness measurement tool to provide an objective operational performance indicator for rural local roads, initiated the development described in this paper, and implemented its use. This paper explains the processes involved in developing and implementing this strategy and the benefits to both the road authority and the company.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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 | 0.001 |
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".