Ship safety and performance in pressured ice zones: captain's responses to questionnaire
Bibliographic record
Abstract
Transport Canada funded a project whose objective is to provide real-time information on ice pressure development to ships operating in the Arctic, in order to minimize safety and operational problems due to such ice conditions. To ensure that the predictive system will provide information that will be of use to the Captains and ship owners and operators, a questionnaire was prepared and distributed to the Captains of vessels that travel through the Canadian Arctic. The main focus of the questionnaire was on topics such as the type of product needed, the geographical regions where pressure ice presents a problem during shipping, and how the product should be distributed to operating vessels. The questionnaire and Captains’ responses are discussed in this report. All Captains agreed that ice pressure can have a significant impact on a vessel’s navigability, and that a product providing information on ice pressure development and build-up, and information on occurrence of leads, will significantly contribute to safe navigation through ice covered waters. In addition to a forecast of where pressured ice develops, the ability to demonstrate when pressured ice is developing in real-time is desirable. The responses identify the zones of interest and factors that can influence ice pressure. The responses also discuss the appropriate forecast products.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".