Mathematics Analysis Method Research for Dependence of Flight Crew Operation Behavior
Bibliographic record
Abstract
As revolution of airspace operation environment(such as ICAO Performance Based Navigation operation) and development of navigation and surveillance infrastructure(ADS-B technology), those responsibilities of both flight crew and air traffic service agency have been changed. The traditional avionics human machine interface and human machine interactive behavior are directly challenged by the monitoring and alerting of en-route navigation performance, appliance and allocation of emergency route and new mission in air traffic management. Meanwhile flight crew workload will also increase. Therefore it is an inevitable trend that the integration level of system and function will be further improved. New direction of system and function integration will be found in terms of dependence analysis between each human machine interactive behavior.This paper is based on serial process hypothesis of human brain, sets RNP APCH profile as operation scenario background, translates flight crew operation behavior into abstract mathematic model and quantitatively produces the level of dependence and strength of workload utilizing mathematic means. The results of test and analysis illustrates that the real human machine interactive behavior can be satisfactorily described by abstract model and quantitative data. A theoretic method for the integration of human machine system has ultimately been explored.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".