Ice-induced global loads on USCGC Healy and CCGS Louis S. St-Laurent as determined from whole-ship motions
Bibliographic record
Abstract
This report provides documentation on the recently updated MOTAN system. The MOTAN uses measured whole-ship motions to obtain the global loads associated with transient ice impacts. Model-scale and full-scale data are used to demonstrate the feasibility of using the MOTAN system to provide reliable information on global loads. Whole-ship motions of the icebreakers HEALY and LOUIS, which have similar ship displacements, are used to determine ice-induced global loads for more than 200 impacts. Results show good agreement between global loads measured on the HEALY and LOUIS. Impacts with warm first-year ice at ship speeds up to 11 kt caused global loads from 1.7 to 7.0 MN. Impacts with medium to thick second-year and multi-year ice at speeds from 0.6 to 16.1 kt resulted in global loads from 1.9 to 17.3 MN. The three highest global loads on the LOUIS (15.7, 16.7 and 17.3 MN) resulted from ramming a rubbled multi-year floe at about 11 kt. The three impacts occurred in quick succession, over a period of about 5 seconds, as the ship slowed to 5 kt from 11 kt. Plotting the initial impact force (the first in a series) versus ship speed showed a clear trend of increasing global load with increasing ship speed, for the multi-year ice events. A lesser correlation was found for thick second-year ice impacts and no apparent correlation for thick first-year and medium second-year ice impacts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".