Asset Service Index as Integration Mechanism for Civil Infrastructure
Bibliographic record
Abstract
One of the challenges in asset management is the integration of asset categories in the decision-making process. Asset management frameworks provide a structure to manage separate categories, and integration is assumed through database design. “Silo” management systems such as pavement and bridge management systems have parallel components that include all major elements of the classic asset management system, yet there is no integration of the results of each component system at the decision-making or expert system stage of the process. Senior decision makers are presented with the outputs of each component system and can clearly see the top priorities for each asset category; however, there is no mechanism for producing a single program list that has been developed with cross-optimization techniques. Trying to establish a multiyear priority program is difficult because of the number of asset categories, the different performance models used by each asset, and the challenges of multilevel optimization. This paper explores the links between silo systems and proposes an integration mechanism for cross optimization that recognizes the unique characteristics of individual asset categories.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".