Overview of Iceberg Detection Capabilities of RADARSAT Synthetic Aperture Radar
Bibliographic record
Abstract
Since the initial CSA ADRO-1 program in 1996, C-CORE has been investigating the capabilities of RADARSAT synthetic aperture radar (SAR) satellite for the detection of icebergs. SAR satellites such as RADARSAT can detect icebergs over very large areas in all weather, regardless of ambient conditions like darkness, rain, and fog. This multiyear program has received support from a variety of sources including the Canadian Space Agency’s ADRO-1 and ADRO-2 programs, the Canadian Ice Service, and a consortium of oil and gas companies operating on the East Coast of Newfoundland. In all, over 40 radar scenes have been collected for this project, with over 20 scenes provided by the Canadian Ice Service. In this paper, an overview of the iceberg validation program will be presented. In addition, some examples of RADARSAT iceberg data will be presented, including detections from Fine mode (8 metre resolution), Wide mode (30 metre resolution) and ScanSAR narrow mode images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".