Incorporating Condition Monitoring for Multi-Faceted Decisions
Bibliographic record
Abstract
The management of naval platforms should be considered in the context of the capabilities that they provide their operational community. A warship, often described as multirole, achieves `flexibility of role' through a complex network of integrated and interdependent systems. When these systems are coupled with monitoring sensors, there is potential to exploit the feedback for online and offline health assessments. The aggregation of these assessments can be conceived of as a multivariate vector corresponding to the capabilities of interest. This vector can be used as the basis of trade-off analysis for the differing courses of action under consideration thus forming part of the decision support environment. Another dimension of trade-offs is the consideration of decision making and planning tiers: tactical, operational, and strategic. Each level has a differing and nuanced understanding of goals such as cost, reliability, availability, and assurance; as such these tiers can find themselves in competition with each other. Condition monitoring provides additional input for dynamic maintenance activity decisions that reflect the evolving organizational context and the tiers' desired outcomes. This paper presents a framework for relating equipment health monitoring on complex naval platforms to a decision support environment consisting of multiple capabilities and competing decision tiers.
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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 | 0.007 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".