Correlation of model-scale to full-scale ice piece size
Bibliographic record
Abstract
This document summarizes the model-scale to full-scale ice piece size correlation performed at the National Research Council’s Ocean, Coastal, and River Engineering (NRC-OCRE) St. John’s Ice Tank. This correlation work supports the NRC-OCRE CCGS Polar Icebreaker model test program; it is based on an earlier investigation by Lau et al (1999) on the influence of ice thickness on piece size during icebreaking by sloping structures. For this study, additional data from the CCGS RClass icebreakers and the USCGC icebreakers Healy and Polar-Star are examined. The study is focused on the scaling performance of NRC-OCRE EG/AD/S model ice with respect to piece size generation. This study has shown the non-dimensional piece size decreases and approaches that found in full scale beyond a certain thickness, i.e., ~ 9 cm. We tested the Polar Icebreaker model in EG/AD/S model ice at 8 cm and 10.4 cm, and at this range we expect the similar thickness dependency follows. However, the bow breaking pattern of the Polar icebreaker model produced much larger pieces in comparison with other more conventional icebreaking bows, i.e., the R-Class, tested in similar ice thickness. It points to a need for further assessment of the bow shape influence on broken piece size, as the Polar icebreaker designs have bow geometry significantly diverse from the traditional icebreaker bow forms that may contribute to different icebreaking patterns and hence piece size.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".