User-centred analysis to inform the development of ice navigation decision support technologies
Bibliographic record
Abstract
A growing interest in northern shipping routes and the transition to autonomous shipping has created an influx of decision support systems and low-level automation technologies for navigation crews in sea ice. While these technologies are intended to support decision-making, they often cause frustration for crews. Researchers theorize that using a user-centred design approach will lead to technologies that are more effective in supporting crew decision-making. This research aims to provide user-centred insights to inform the development of future ice navigation decision support technologies. The study reports a thematic analysis of unstructured interviews conducted with crew onboard a Canadian Coast Guard icebreaker. The interviews focused on the question: How do navigators decide on route planning and navigation in sea ice? The thematic analysis codes are used to create a Hierarchical Task Analysis (HTA) that models the process of route planning and ice navigation onboard this vessel. The HTA is used to identify potential issues and areas where technology can better support ice navigation decision-making.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".