The Detection of Multi-Year Ice Using Upward Looking Sonar Data
Bibliographic record
Abstract
Upward looking sonar (ULS) instruments on year-long sub-surface moorings are widely used in support of oil and gas exploration programs. The analysis results are used to provide key inputs to the engineering of offshore platform design and ship-based ice management. Detection of the older and harder multi-year sea ice is particularly important for engineering and ice management applications. Here, we analyze multi-year ULS measurements of sea ice in the Beaufort Sea and off Northeast Greenland. The detectability and characterization of multi-year ice is derived from two independent analysis methods. The first method uses the backscattered acoustic pulse shape received by the sonar instrument while the second method involves the degree of the smoothness of the underside of the ice keels away from the leading and trailing edges. Both methods demonstrate skill in detecting multi-year sea ice as distinct from first year sea ice. The two methods are shown to be complementary in that some multiyear ice floes cannot always be clearly categorized by one method alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".