Bibliographic record
Abstract
Managed ice is the term given to ice that has been broken ahead of a platform or anchored vessel in order to reduce the ice loads or other effects of ice interaction such as rubble build up. Ice features which would create loads greater than the mooring loads of the stationary platform or vessel are broken into small pieces to reduce the ice loads. This is usually done with several icebreakers; one or more breaking ice in the far field and one or more breaking ice in the near field. Although there is some experience on how much management is required to reduce mooring loads, the present methods for managed ice loads rely on expert judgment and are empirical. In this paper, simple concepts for managed ice loads have been developed and quantified. Ice load concepts and equations are presented based on piece size of the managed ice, ice and rubble thickness, and whether there is any ice pressure in the ice field. These concepts can be used to specify piece sizes to be achieved and operational tactics in various ice management scenarios for a given floating platform and mooring system. Comparisons are made with the Kulluk experience and loads in the Canadian Beaufort Sea in order to help verify the equations and calibrate the inputs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".