Gaze behavior of spotters during an air-to-ground search
Bibliographic record
Abstract
Crashed aircraft must be located quickly to minimize loss of life, often requiring visual search from the air. This study was designed to develop methods for evaluating the gaze behaviors of spotters during air-to-ground search and to compare field derived measures with similar lab measures reported in the literature. A secondary aim was to assess adherence to a prescribed scan path, evaluate search effectiveness, and determine the predictors of task success. Eye movements were measured in 10 volunteer spotters while searching from the air for ground targets. Static visual acuity at several eccentricities and contrast levels and performance on a lab-based search performance were also measured. Gaze relative to the head was transformed to gaze relative to the ground using information from the scene. Coverage and task success were similar to literature values from a lab-based study of air-to-ground search. Air search task success could be predicted best from a combination of gaze and laboratory variables and, like previous lab-based research, experience was not one of them. Results from this field study provide some support for the generalizability of lab research. In both lab and field research performance is quite poor. Future improvements in air search and rescue success will depend upon improvements in training, the refinement of scan tactics, changes to the task methods or environment, or modifications to parameters of the search exercise.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".