Flexible modeling and simulating mission availability within the operational framework for Canadian Naval platforms
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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