Full scale experience with Kulluk stationkeeping operations in pack ice (with reference to Grand Banks developments)
Bibliographic record
Abstract
This report addresses the question of moored vessel stationkeeping operations in pack ice, on the basis of full scale experience with the Kulluk in the Beaufort Sea. As part of this work, a data base which documents full scale ice load levels on moored vessels has been significantly extended, and now includes almost 700 individual ice loading events. In addition, more operationally oriented information about ice management support activities and levels of risk (alerts) has been blended with the load data, for each event. Various scatter plots of expected ice loads in managed pack ice conditions are presented. Data relating to the effect of different levels of ice management support on load and risk levels is also included. The implications of this information are outlined in relation to various moored vessel system operations in Grand Banks pack ice conditions. It is shown that moored vessel operations in the type of pack ice conditions periodically encountered on the Grand Banks should be less difficult than is currently perceived, provided systems with reasonable in-ice capabilities and adequate levels of ice management support are used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".