Relationship between Microwave-Derived Snow Thickness on Winter First-Year Sea Ice and Melt-Pond Fraction
Bibliographic record
Abstract
Early summer melt pond fraction is predicted using late winter C-band backscatter of snow-covered first-year sea ice. Considering the association between melt pond fraction and winter were collected during the 2012 field campaign in Resolute Passage, Nunavut, Canada on relatively smooth first year sea ice to estimate the aerial melt pond fractions. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter. The correlations between the aerial melt pond fractions and late winter SAR parameters (e.g., linear and polarimetric) and texture measures derived from SAR parameters, are utilized to develop multivariate regression models. These regression models are finally employed to predict melt pond fractions. Results demonstrate substantial capability of the regression models to predict melt pond fractions at near range and far range incidence angles (RMSE = 0.11), compared to the mid-range (RMSE = 0.16). These predictions also act as a proxy to estimate the snow thickness variability, as higher pond fraction evolves from the thinner snow cover. We also found that the strength of the regression models enhances when we combined SAR parameters with texture measures. The results also indicate that at far range incidence angles, SAR polarimetric data are needed, whereas for near range and mid-range, linear SAR data are adequate.
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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 | 0.001 |
| 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 | 0.000 |
| 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".