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Record W2156703647 · doi:10.1518/001872007x215746

Gaze Behavior of Spotters During an Air-to-Ground Search

2007· article· en· W2156703647 on OpenAlexaff
James L. Croft, Daniel J. Pittman, Charles T. Scialfa

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2007
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVisual searchTask (project management)GazeSearch and rescueSimulationComputer scienceArtificial intelligenceEngineeringRobot

Abstract

fetched live from OpenAlex

OBJECTIVE: 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 previous lab results. Secondary aims were to assess adherence to a prescribed scan path, evaluate search effectiveness, and determine the predictors of task success. BACKGROUND: Crashed aircraft must be located quickly to minimize loss of life, often requiring visual search from the air. METHOD: Eye movements were measured in 10 volunteer spotters while they searched from the air for ground targets. Visual acuity, contrast levels, and performance on a lab-based search task were also measured. RESULTS: Results were similar to those of previous lab-based studies of air-to-ground search. Task success could be predicted best from a combination of gaze and laboratory variables, and as in previous research, experience was not one of them. CONCLUSIONS: In both lab and field research, performance is poor. 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. APPLICATION: Spotters were unable to reliably search their assigned area, which has implications for the current search training program and in-the-air protocol.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.050
GPT teacher head0.352
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2007
Admission routes1
Has abstractyes

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