Operational Readiness Simulator: Optimizing Operational Availability Using a Virtual Environment
Bibliographic record
Abstract
The maintenance and logistics systems that support aircraft fleets are complex and often very integrated. The complexity of these systems makes it difficult to assess the impact of events that affect operational capability, to identify the need for resources that can affect aircraft availability, or to assess the impact and potential benefits of the system and procedural changes. This problem is further complicated by the adoption of condition-based maintenance approaches resulting in dynamic maintenance planning as maintenance tasks are condition directed instead of scheduled or usage based. A proof of concept prototype for an aircraft operational readiness simulator (OR-SIM) has been developed for the Canadian Forces CH-146 Griffon helicopter. The simulator provides a synthetic environment to forecast and assess the ability of a fleet, squadron, or aircraft to achieve desired flying rates and the capability of the sustainment systems to respond to the resultant demands. The prototype was used to assess several typical scenarios including adjustment of preventative maintenance schedules including impact of condition-based maintenance, variation of the annual flying rate, and investigation of deployment options. This paper provides an overview of the OR-SIM concept, prototype model, and sample investigations and a discussion of the benefits of such an operational readiness simulator.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".