The Identification of Extreme Ice Features in Satellite Imagery
Bibliographic record
Abstract
Abstract P>Sea ice monitoring is an important field of scientific research and relevant to operational applications. One of the major engineering challenges in undertaking production developments in Arctic offshore regions is the frequent presence of extreme ice features that pose a hazard to facilities and surrounding subsea infrastructure. The information on extreme ice features (i.e., ridges, icebergs etc.) is important from the standpoint of potential ice load levels on fixed structures and ice scouring of seafloor facilities. Satellite observation has been shown to be useful for extracting and characterizing ice regimes. Sea ice can be monitored using satellite imagery acquired by different types of sensors: microwave radiometer, optical instrument and synthetic aperture radar (SAR). The outputs of sea ice monitoring may include various ice parameters such as edge, thickness, concentration, classification, iceberg detection, and ice statistics. This paper describes application of high and low resolution SAR imagery for sea ice monitoring and to resolve local features and extend the statistical baseline to larger regions because extreme ice features may be invisible or ambiguous with other ice features in these data. The use of higher resolution imagery allows for easier detection of ice features and provides sufficient spatial detail necessary for detecting ice features from sea ice, identification and estimation of size and geometry of ice floes and icebergs. It was demonstrated that SAR sensors with multiple resolution lead to a better understanding of ice conditions including ice edge, concentration, floe statistics, and other ice features such as icebergs. A technique based on SAR interferometry was used for identification of iceberg in sea ice as well as for extracting iceberg topography.
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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 | 0.002 | 0.001 |
| 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".