Local Design Pressures for Structures in Ice: Analysis of Full-Scale Data
Bibliographic record
Abstract
The design of structures for ice conditions requires knowledge of local ice pressures to allow for appropriate levels of structural strengthening. Full-scale field data are key to enhancing our understanding and modeling of ice behavior. Data collected during icebreaker ramming events represent an important source of information for use in the evaluation and validation of design methodologies. This paper examines several ship-ice interaction datasets using the ‘event-maximum’ method of local pressure analysis developed by Jordaan and co-workers [1]. In this method, the local pressure is obtained from a normalized curve which contains two parameters, α and x0. Local pressure analysis results for data from the USCGS Polar Sea, CCGS Terry Fox, CCGS Louis St. Laurent, and Swedish Icebreaker Oden are presented. For all data considered the calculated values of α fall below the design curve. A discussion of panel exposure, event duration and the effect of these factors on x0 is given. New design curves are presented. A comparison of results with those obtained using an alternative approach, the up-crossing rate method, is presented in a companion paper [2].
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".