A Delphi study to formalize domain knowledge on maritime collision avoidance and to inform training
Bibliographic record
Abstract
Maintaining effective situation awareness (SA) is a key facet that experienced Officers of the Watch (OOWs) build to practice safe collision avoidance and sound Bridge Resource Management (BRM). Novice OOWs lack the experience to efficiently use sources of information on the bridge to build and maintain SA. This research takes a human centred approach though a Delphi study to answer the following question: Can consensus be reached between domain experts to create a training tool to increase SA for collision avoidance amongst new watchkeeping officers? The study is grounded in Endsley’s model of SA and the Collision Regulations (COLREGs). A mixed methods approach is used and includes a series of surveys administered through Qualtrics to elicit opinions from experienced seafarers as they relate to collision avoidance, SA information requirements, sources of information from bridge equipment, and BRM. The goal was to create a generalizable sequence, in the form of a flowchart, for efficiently collecting information, supported by consensus and validation from the experts. Through this research, consensus was achieved to create a flowchart for collision avoidance information gathering. This flowchart was further validated through semi-structured interviews with subject matter experts. The outcome of this research may impact the training and operational sectors of the maritime industry by informing formal instruction and on-the-job training and evaluation.
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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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".