Supporting shipboard helicopter flight testing with simulation and metrics for predicting pilot workload
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
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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