Station-Keeping Trials in Ice: Overview of Ice Management Support
Bibliographic record
Abstract
In March 2017, Statoil performed station-keeping trials in drifting ice in the Bay of Bothnia. The anchor handling tug supply vessel Magne Viking, performed station keeping operations in various ice conditions, including managed and non-managed ice. Physical ice management was used to manage the approaching ice to a target condition suitable for the station keeping tests, and to enable other essential operations including deployment and retrieval of the mooring spread and other equipment. Given the objective of the trials, physical ice management activities were performed in such a way to allow investigation of various relevant parameters that influence the managed ice condition. Additional tests were also performed for the sole purpose to assist with validation of Aker Arctic’s ice management software “AIMS”, including tests designed to estimate the performance of the vessels under different ice conditions. This paper focuses on the physical ice management operations performed by the ice management vessel Tor Viking (TV) during the Station Keeping Trials in ice (SKT). Also included is a discussion on how AIMS was used in the planning phase and how simulations compared with actual observations.
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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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".