A System for Measuring Ice-Induced Accelerations and Identifying Ice Actions on the CCGS Amundsen and a Swedish Atle-Class Icebreaker
Bibliographic record
Abstract
When ships operate in the Arctic, sea-ice induce an additional environmental load on the vessel. The ice load can vary significantly depending on the dominating ice-breaking failure mode. In this work a sensor system for measuring ice induced accelerations on the Canadian icebreaker CCGS Amundsen and a Swedish Atle-class icebreaker is presented. The sensor system consists of low-cost inertial measurement units. Ship-ice interaction data has been collected during expeditions along the coast of Labrador in Canada and in the Greenland Sea north of the Norwegian Svalbard archipelago. Depending on the failure mechanism of the interacting ice, vibrations at different frequencies are induced into the icebreaker ship. A time-frequency decomposition based on the Wigner-Ville distribution has been modified such that it is applicable to analysis of ice-load induced acceleration signals. Based on the frequency pattern of the induced vibrations, this novel method allows for evaluation of the intensity of the ice-loads and identification of the dominating ice failure mechanism, which is demonstrated for several ship-ice interaction events. The presented novel time-frequency decomposition for ice induced accelerations is a powerful tool for the identification of the threat imposed by sea-ice to a structure. In further work the time-frequency decomposition will be used as feedback in ice-capable control and monitoring systems for Arctic offshore operations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".