USING A HELICOPTER-BORNE EM-INDUCTION SYSTEM TO VALIDATE RADARSAT SEA ICE SIGNATURES
Bibliographic record
Abstract
Field surveys over the past several winters in the Gulf of St. Lawrence and off Labrador use helicopter-borne sensors to validate SAR ice signatures in RADARSAT imagery. Ice-plus-snow thickness profiles were collected using an electromagnetic (EM) induction system towed 15-25m above the ice surface by helicopter. Measurements from the laser altimeter contained in the EM system were high-pass filtered to derive ice surface topography profiles. In RADARSAT images, changes in SAR backscatter values are usually associated with changes in EM-measured ice thickness and laser-measured surface roughness. For example in the Gulf of St. Lawrence, large floes having low SAR backscatter are associated with uniform EM-measured ice thicknesses of 30-50 cm, while more variable ice thicknesses are present in the surrounding areas having higher backscatter. EM-measured ice thicknesses representing both deformed and undeformed ice are 50 % higher than ice thicknesses obtained through augered ice holes in undeformed ice. In a SAR image of the Labrador shelf area, the inshore ice appears dark, with bright streaks visible southeast (downwind) of small coastal islands. These streaks correspond to ice rubble with EM-measured ice thicknesses of about 1-2 m and laser-measured ridge elevations up to 0.9 m. 1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".