Decision Models To Prioritize Maintenance And Renewal Alternatives
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
Publicly owned infrastructure forms a large portion of the existing infrastructure in Canada and elsewhere. Typically, asset managers must make decisions about maintenance and renewal alternatives based on sparse data about the current state of their infrastructure assets, the relative risk of failure of these assets, and the life cycle costs of proposed interventions. The infrastructure in any one organization can consist of a diverse set of assets ranging from bridges to buildings and from buried to overhead utilities. The optimal selection of proposed interventions across this broad spectrum of assets is also problematic, and currently it is performed in a subjective manner. This paper identifies a number of prioritization techniques that can be used to compare and rank repair and renewal projects. This research does not attempt to develop models to select the 'correct projects' or even to identify the best decision model or prioritization technique; rather, it attempts to illustrate by example the results of specific decision-making paradigms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it