ADJACENCY MODELING TOOL FOR CIVIL INFRASTRUCTURE ASSETS
Bibliographic record
Abstract
Managing public assets involves a systematic process of operating, maintaining, and upgrading physical assets in a cost-effective manner. In many municipalities and provincial transportation and public works departments, it is common to see rehabilitation work on an asset, say x which often ignores the condition of the adjacent assets (say w, y, z, etc). As one or more of the adjacent assets (w, y, z, etc) deteriorate, the need to carry rehabilitation work on them arises within a short of time, after the completion of the rehabilitation work on asset x. As a result, the subsequent rehabilitation work on adjacent assets w, y, z, etc. often requires partial removal and repair of asset x to allow the completion of this new rehabilitation work. The effects of such subsequent rehabilitation works include premature damage to the recently rehabilitated asset, increased deterioration of such asset, frequent disruptions of service to users and increase in rehabilitation costs. The adjacency modeling tool provide a context within a GIS and TAMWORTH (Transportation Optimization Software) that assists users to identify and analyze any need to rehabilitate adjacent assets 1 Kachua/Mrawira/Ming at the same time. Instead of focusing on any single element for a priority project, the adjacency model seek to organize and analyze all the adjacent assets or facilities into work-zone corridors within the road network to allow a more comprehensive planning and management of undertaking rehabilitation work and develop a process by which rehabilitation work can be scheduled once for a given corridor whenever technically viable and economically justifiable. A case study is presented to show its effectiveness with road and bridge network data in New Brunswick, Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".