Exploring the Experiences of Pilots within Canadian General Aviation Flight Operations
Bibliographic record
Abstract
Pilots track flight hours as a quantitative measure of expertise. This linear development of expertise may apply to technical skills; however, it has been suggested that the development of nontechnical expertise is associated with operational exposure to threats and errors Within this framework, nontechnical skills may develop at different rates depending upon exposure to different threats and errors within specific types of flight operations. The present investigation examined the threats, errors, and nontechnical skills of pilots within Canadian general aviation operations. One hundred thirty narratives describing real-world scenarios were gathered from pilots with an online self-report Hangar Talk Survey (HTS). Several threats, errors, and nontechnical skills were significantly associated with specific types of operations. This suggests that the rate of nontechnical skill development may additionally be linked to the type of operation a pilot is involved in, rather than to the number of flight hours alone.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".