Performance evaluation of quad-pol data compare to dual-pol SAR data for river ice classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Satellite SAR data are a unique source of information about river ice since the microwaves penetrate through clouds as well as snow and ice cover. The influence of the number of polarization channels on the nature and amount of information is, however, not yet fully investigated. The article intends to compare quad-pol and dual-pol data. The studied areas include two rivers with different types of ice cover – the Peace River in Canada and the Vistula River in Poland. We used RADARSAT-2 quad-pol Single Look Complex (SLC) data. The comparison methods include separability analysis (Hellinger distance, Bhattacharyya distance) and Wishart supervised classification. We found that dual-pol and quad-pol data provide equivalent information for homogeneous ice cover (overall classification accuracy above 80% for all polarization modes). Differences were observed in case of complex river ice cover with high diversity of ice types.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it