A Statistical Approach for Estimating Sea Ice Thickness
Bibliographic record
Abstract
Abstract Sea ice reporting and ice charts are a necessary simplification of highly variable sea ice conditions over large areas. Ice conditions are typically shown using egg codes. These codes can be used to determine an equivalent ice thickness as a method for calculating a ship’s ice resistance. This approach does not acknowledge the variability within the chosen region, such as the presence of ridges, ice pressure and snow cover. A new statistical method is proposed to use live capture data including the speed and resistance from a medium ice breaker to estimate the changing ice thickness. The thickness is calculated using the ship’s regression model for the ship’s performance based on model scale tests. Measured thrust data from the ship is used in conjunction with the thrust deduction factor and open water resistance to derive the ice resistance and subsequent thickness. The statistical ice thickness is compared against the equivalent ice thickness derived from the ice egg codes to determine the accuracy of current ice reporting and identify the variance within a defined region. This statistical approach may also assist in identifying features such as ridges, including the frequency of encountering these events. This analysis has high potential for sources of error, such as the changes in force required to bend and break ice sheets. Therefore, the data must be adequately analyzed to eliminate such noise while preserving the ability to identify changes to ice thickness. The potential implications of this research are an improved awareness of the variability in sea ice, and improvements to the methods for reporting and characterizing sea ice conditions.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".