Ice Management for Steering of Large Ice Floes
Bibliographic record
Abstract
A study has been carried out to assess the feasibility of using ice management to influence the drift trajectories of potentially hazardous ice floes. A large ice floe drifts under the action of wind and water drag, Coriolis force, and the forces exerted by the surrounding ice cover. For a massive ice floe, the force required to divert the drift trajectory away from an offshore installation would be far beyond the capabilities of available vessels. However, such trajectories may be influenced by selectively breaking the ice in the vicinity of the floe. The present investigation employs a numerical model to simulate the drift of large floes embedded in an ice cover. The model is based on solving a set of equations that describe the conservation of mass and momentum, and constitutive equations of the ice cover. The large floes are considered to move as rigid bodies. The study consists of validation tests and evaluation of the effectiveness of several ice breaking scenarios. A validation test considers the drift of large ice floes from the Chukchi Sea. Predictions of floe drift and deformation modes of the ice cover are compared to observations obtained from available imagery. The investigation next examines the effectiveness of scenarios of ice breaking. Selected options of ice breaking around a large floe are introduced by altering ice cover concentration. The results indicate that ice management can alter the trajectories of large hazardous floes.
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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".