A Study on the Influence of the Navigator's Personal Characteristics on the Perceived Collision Risk in Close-quarter Situations
Bibliographic record
Abstract
This study focuses on the margin of human error when a navigator is embarrassed by the psychological fear of collision in a close-quarter situation (CQS) and is unable to perform as per the prescribed collision avoidance measures. The purpose of the study is to identify the effects of the navigator's personal characteristics or factors in relation to on-board career (OC), license rating (LR), and age on the perceived collision risk (PCR) in CQSs. In order to obtain quantified data regarding the collision risk perceived by the navigator in four typical CQSs between their own ship and a target ship, this study measured and collated the heart rate variability of 30 navigators on their own ship when two ships approached each other at a speed of 10 knots from 2.5 nautical miles to a collision situation. According to a multiple regression analysis of the measured values, the navigators' OC and LR factors had negative effects on the PCR, while the age factor had no significant effect on PCR. The t-test results showed that the PCR value was significantly higher for navigators with an OC ≤ 4 years than for those with an OC ≥ 5 years, and the LR factor was significantly higher for a class 4∼6 group than for a class 2∼3. This finding may be applied to the development of collision risk warning systems, particularly for navigators.
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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 | 0.001 | 0.005 |
| 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".