Estimation and Validation of Floe Size Distribution from Upward Looking Sonars
Bibliographic record
Abstract
Abstract Moored upward looking sonars (ULS) have been used extensively for over twenty years to measure sea ice draft thicknesses and ice keel widths. They have rarely been used to analyze ice floe sizes. In 2015, Statoil Canada, ArcticNet, the Research & Development Corporation of Newfoundland and Labrador (RDC) and Husky Energy partnered in an offshore research expedition, a component of which was Ice Profiler Sonar (IPS) and Acoustic Doppler Current Profiler (ADCP) measurements in waters off Newfoundland. This provides an excellent opportunity to develop methods to estimate floe size distributions in the marginal ice zone. IPS data is typically analyzed for ice draft and for the presence and absence of sea ice. ADCP bottom tracking data during periods of high ice concentrations provides direct measurement of ice drift. Deriving these ULS-based parameters in the low concentrations and often energetic wave environment of the marginal ice zone is difficult. A six-day period of relatively low wave energies was analyzed for ULS derived ice floe sizes. Over 1000 floes were detected with most of the detected widths being less than 30 m and a peak in the distribution at less than 10 m. Ice concentrations and ice drifts as derived from the ULS were similar to those reported by Canadian Ice Service daily ice charts. Analysis of both theoretical and natural ice floe shapes suggests that the average of the ULS determined ice floe widths is typically about 70 to 80% of the equivalent diameter and about 55% of the typical maximum horizontal extent. Thus, much of the ULS detected floes were likely smaller than the resolution of satellite imagery. As the ULS moorings measure ice draft every one or two seconds and ice speeds every one minute, estimates of average floe mass, momentum and energy of ice features observed during the six-day analysis episode were possible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".