Analysis of Survey Data on Situation Awareness of Helicopter Pilots
Bibliographic record
Abstract
According to the U.S. National Transportation Safety Board, from 1989 to 1992, situation awareness (SA) was a major factor causing 80% of all aircraft accidents in scheduled airlines. Therefore, the prevention of accidents through effective training in SA became a pivot in aviation safety. During the past 10 years, since all helicopter accidents in South Korea were caused by factors related to SA, an appropriate counter-measure was required. A study used survey data to examine various factors related to SA that could affect helicopter pilots. Recognition of and countermeasures for those factors in emergency situations were analyzed. The results show that although factors associated with SA and vigilance have lower correlations with each other, factors associated with recognition, diagnosis, and generation and implementation of solutions have higher correlations with each other. Thus, the results demonstrate the need for better SA through educational training. Also, there were no significant differences among factors related to proficiency, procedure, acquaintance, anticipation, and comprehension between instructor pilots and copilots. However, there were significant differences regarding mentality, position information, experience, and preparation. These results highlight the differences between instructor pilots and copilots derived from the acquisition of knowledge and flying experience.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".