Flexible modeling and simulating mission availability within the operational framework for Canadian Naval platforms
Bibliographic record
Abstract
Availability and reliability metrics have become key in-service performance measures in Canadian defence contracting. Previous implementations have evolved due to challenges in application, and were focused on the Air Force operational environment. With ongoing capital procurement and in-service support contracting, the Navy requires a definition and method of assessing availability appropriate to Naval platforms. Naval ships are multi-role multi-function platforms. Traditional single function availability metrics are ambiguous for multiple functions / capabilities. Critical systems (e.g. propulsion, power) have an obvious effect on availability, while the loss of other functions (e.g. radar) do not. Non-critical system and capability impact is a function of the requirements of the current mission, thus mission availability must be evaluated. Mission availability for a multi-function platform was defined as the interval average evaluation of critical system availability, mean capability availability, and mean weighted performance availability. The latter linked engineering performance to expected operational performance. Mission Capability Configuration Reliability Model was introduced to link system performance to capability performance. Using this model, an availability simulation, incorporating failure, maintenance, and logistical models was developed to assess mission availability. The simulation was applied to the project management functions of ship design and specification prototyping, availability assessment for contract management, and in-service performance prediction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".