DEVELOPMENT OF GENERIC AIRCREW MEASURES OF PERFORMANCE FOR DISTRIBUTED MISSION TRAINING
Bibliographic record
Abstract
Abstract : Advances in technology have made simulation and, latterly, distributed mission simulation valuable additions to the training of aircrew. Simulation is widely accepted by the aviation community and much research exists to show the benefits and most profitable applications of simulation. Distributed mission training represents an enhancement to simulation although at this point it is unproven exactly what training objectives should be associated with it and what additional benefits will accrue when compared to traditional simulation or flying training. This project developed generic measures of performance for application to distributed mission training exercises. The application of these measures of performance will allow training organisations to make valid statements about the benefits of distributed mission training and informed decisions to be made regarding which training objectives to address through the use of distributed mission training. Humansystems Incorporated(registered tradename) were tasked with reviewing literature provided by DRDC Toronto in order to identify potential measures of performance. In particular, the Scientific Authority was interested in measures of mission planning, mission execution, mission debriefing, situation awareness and the change in aircrew knowledge structures, as relevant to distributed mission training. A measurement model has been developed that includes a conceptual outline of a CF-18 mission, a behavioural hierarchy composed of domains, categories and specific behaviours, and a range of associated rating scales and objective measures. Additionally, a trial plan for the application of one particular measure (Pathfinder -- description and measurement of knowledge structures) has been developed.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".