A quantitative approach for determining pilot affective patterns during soaring flight simulation
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
The development of affective computing system for soaring flight simulation training requires a mechanism for determining pilot affective patterns. These patterns may be comprised of multiple elements, including emotion, pilot performance level, and physiological responses. This article proposes an approach to quantify these elements during the performance of flight training maneuvers. To validate this, a sample population of pilots performed flight tasks in a soaring flight simulator while wearing physiological sensors. Pilots reported subjective emotional stress using a 10-point numeric scale. A 5-point numeric scale was developed to transform flight task performance criteria into a unified scale. The use of common quantitative units for emotion and performance provides a simple mechanism for computational analysis and comparison of values that would otherwise be either qualitative (i.e. emotion) or measured in different units (i.e. flight tasks). The research provides a foundation and initial data set for the development of affective flight training systems that adapt to operator affect and performance in real-time. During analysis, asymmetric EDA responses were observed (left vs. right side sensors).
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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