Observations of sea ice thickness, surface roughness and ice motion in Amundsen Gulf
Bibliographic record
Abstract
Ice thickness and surface roughness measurements of first‐year (FY) sea ice were collected with a fix‐mounted helicopter‐borne electromagnetic (HEM) ‐laser system in Amundsen Gulf in April to May 2004. The modal ice thickness values are in good qualitative agreement with different ice types identified in synthetic aperture radar (SAR) imagery and shown on ice charts produced by the Canadian Ice Service. Modal ice thickness values which generally represent level ice thicknesses were about 2.0 m over landfast ice. A large range of modal ice thicknesses was observed in the mobile ice region, with values of about 0.2 m (young ice) in leads (where there was high radar backscatter), 0.6 m (thin FY ice) in the polynya (where there was medium to high backscatter), and about 1.1–1.9 m (thick FY ice) elsewhere. High surface roughnesses are strongly associated with high radar backscatter in SAR imagery, and are observed in areas of large shear. The ratio of the standard deviations of ice draft and averaged roughness in an area of landfast ice is in good agreement with the ratio of the standard deviations of ice draft and ice‐equivalent roughness expected from isostasy, with constant level ice and snow thickness. However, the standard deviation of ice‐equivalent roughness may be significantly underestimated, due to differences in snow thickness between level and deformed ice, and limitations of the laser processing method. Modal ice (plus snow) thicknesses measured with the HEM system are within the range of historical values measured at Cape Parry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".