Analysis of consistency in first-year sea ice classification potential of C-band SAR polarimetric parameters
Bibliographic record
Abstract
The consistency in first-year sea ice classification potential of C-band SAR polarimetric parameters was analyzed by comparing the results of two studies conducted for the same ice types under different geophysical settings. The SAR images used in the comparison were acquired at an incidence angle difference of 4°. Probability density functions, grey level parameter images, and classification statistics derived using k-means classifier were used in the comparative analysis. The investigation showed that not all polarimetric parameters exhibit consistency in their classification performance under different geophysical settings. Out of the 20 polarimetric parameters analyzed, 12 demonstrated high levels of classification consistency between the two studies. Among these 12 parameters, only four possessed high classification accuracy and could be applicable for sea ice classification under variable environmental conditions. The parameters that showed the highest classification accuracies in both the studies were found to be inconsistent in their ice type separation capabilities. The signatures of these parameters differed for one or more ice types when compared between the two studies. The utility of these parameters in individual sea ice classification studies is recommended but their relevance in generalized sea ice classification scheme is unclear.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".