SAR Image classification of first-year ice types, Bay d'Espoir, Newfoundland and Labrador
Bibliographic record
Abstract
Sea ice during spring melt and breakup can prove dangerous to infrastructure located in the coastal zone. Industries such as aquaculture, which houses much of its infrastructure in the near shore environment, are at the mercy of sea ice motion. Knowledge of the condition of ice during the melt season could allow such users of the coastal zone to plan against potential damage due to the premature breakup of the ice coverage. -- Traditional methods of sea ice detection and classification have been limited to the open ocean and dedicated to problems associated with navigation. Their primary concern is in the identification of first-year ice, multi-year ice and open water. -- The use of second order texture measures, along with a new approach to histogram characterization, and neural network classification have allowed the classification of a fine beam mode RADARSAT image to map five sub classes of first-year sea ice – brash, puddle, flooded, rotten, snow covered, – and open water. Classification accuracies achieved are on the order of 60% with user's accuracies for several of the ice types approaching 100%.
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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 | 0.001 | 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) | 0.002 | 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".