The effects of Augmented Reality on operator Situation Awareness and Head-Down Time
Why this work is in the frame
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Bibliographic record
Abstract
A lack of navigator's Situation Awareness (SA) is one of the leading causes of maritime accidents. Visually observing the area surrounding a vessel continues to be a critical aspect and best practice of safe navigation to establish and maintain SA. Augmented Reality (AR) allows the placement of information in a user's field of view, which can encourage navigators to spend more time looking up at their external environment whilst still having access to operational data. However, empirical evidence on the impact of AR on maritime operations is limited. This paper investigates the effects of AR on navigator SA & Head-Down Time (HDT) using a within-group quasi-experimental design. Seventeen licensed navigators and nautical students analysed twelve navigation scenarios: six non-AR (control) and six AR (experimental) scenarios using a maritime training simulator. SA was measured via SAGAT scores for each scenario and the SA-SWORD to compare preferences. Each scenario was video recorded and analysed for participant's total amount of HDT and head-down occurrences in each scenario. Results found that the addition of AR significantly reduced participant HDT (by a factor of 2.67) and head-down occurrences (by 62%) in comparison to navigation scenarios without AR. Furthermore, AR did not significantly improve mean SA. This study contributes to the limited empirical data on the effects of AR on operator performance, demonstrating the potential value of AR for improving SA and facilitating increased head-up time during maritime navigation, which in turn could improve safety at sea.
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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 | 0.001 |
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 it