Bibliographic record
Abstract
Availability is a system performance parameter which provides insight into the probability that an item or system will be available to be committed to a specified requirement. Depending on the application, availability can be defined to include reliability, maintainability and logistic support information. For fleet management purposes, the ability to quantify availability in terms of all of its contributing elements is essential. This paper provides a discussion on a steady state operational availability model which can be used to assist the Canadian Air Force in its aircraft fleet management requirements. The availability model embodies scheduled and unscheduled maintenance and allows for impact analysis using in-service maintenance data. The model is sensitive to fleet size, aircraft flying rate, frequency of downing events, aircraft maintainability, scheduled inspection frequency, and scheduled inspection duration. The predictive capability of this availability model is currently providing the Canadian Air Force with a more sophisticated maintenance analysis decision support capability. In order for this paper to be available for general distribution, it must be unclassified. As a result, the case studies presented do not reveal the actual operational availability of any Canadian Air Force fleet. However, the level of detail provided is more than adequate to illustrate the case studies and give insight into
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.
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 | 0.000 | 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 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".