Situation Awareness in Fast Rescue Crafts Operators—A Simulator Study
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
This study investigated whether experience in maritime operations contributed to situation awareness (SA) and confidence among Fast Rescue Craft (FRC) operators during simulated maritime search and rescue (SAR) missions. A total of 20 novice and 20 experienced Canadian Coast Guard personnel were presented with collision avoidance scenarios of various difficulty levels on a desktop FRC simulator. A goal-directed task analysis (GDTA) was conducted to identify the critical goals, decisions, and information requirements underpinning FRC operations, providing a structured basis for scenario design and SA measurement. The results indicated that experienced operators had significantly higher Total SA scores. These differences were primarily attributable to stronger performance on Level 3 SA across all scenarios and Level 2 SA in head-on scenarios. Experienced participants also reported higher confidence in Level 1 and Level 2 SA, although no differences were found in Level 3 or Total SA confidence. Experienced operators’ navigation decisions were influenced by informal decision-making cues, especially when interpreting collision-avoidance regulations. The absence of significant differences in Level 3 SA confidence and Total SA confidence between experienced and novice operators suggests that the latter may be overconfident in predicting future events in complex maritime environments. To better prepare novice operators for real-world SAR operations, these findings suggest the potential value of training interventions that focus on specific SA components, particularly projection, and support the development of decision-making strategies under uncertainty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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