Supporting shipboard helicopter flight testing with simulation and metrics for predicting pilot workload
Bibliographic record
Abstract
Shipboard helicopter operations are much more challenging and complex than land-based operations due to many factors associated with the presence of the ship. To determine those conditions in which safe operations may occur, a First of Class Flight Trial (FOCFT) is conducted for every new ship–helicopter pair. This trial results in a Ship–Helicopter Operating Limit (SHOL) envelope that is used to document operational limits for regular operations. Conducting a FOCFT is a, expensive, and time-consuming task that requires testing all aspects of operations. Modeling and simulation efforts to support shipboard helicopter operations have been ongoing internationally for many years with the intention of de-risking FOCFT and introducing efficiency into the testing process. Canada will be accepting several new ship classes into its fleet over the next two decades. In support of FOCFT for these new ships, modeling and simulation tools are being developed by the National Research Council (NRC) Canada and Defence Research and Development Canada (DRDC) and significant advancements have occurred in the past decade. As part of this work, NRC and DRDC now use a framework and analysis approach that is intended to standardize SHOL testing with the use of modeling and simulation. This paper introduces that framework and gives details on the modeling and simulation tools that can be used to reduce risk and increase efficiency for Canada’s upcoming FOCFTs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".