Geophysical controls on C band polarimetric backscatter from melt pond covered Arctic first‐year sea ice: Assessment using high‐resolution scatterometry
Bibliographic record
Abstract
Geophysical controls on C band polarimetric backscatter from the discrete surface cover types which comprise advanced melt first‐year sea ice (FYI): snow covered ice, bare ice, and melt pond; are assessed using polarimetric radar scatterometry from test sites representing high Arctic and marginal ice zones in the Canadian Arctic. Surface characterization data is used to evaluate the interaction of polarized radiation with each feature, and dominant scattering mechanisms are assessed in a regional context. High‐resolution time series (diurnal) scatterometry and coincident atmospheric boundary layer profile data are used to explain linkages between ice‐atmosphere interactions and polarimetric backscatter in a marginal ice zone. The co‐polarization ratio for FYI melt ponds is shown to be distinct from snow covered ice or bare ice during early and peak phases of advanced melt, making it a candidate parameter for the unambiguous detection of pond formation and the inversion of melt pond fraction. The ratio displays an increasing trend with radar incidence angle in a manner consistent with Bragg surface scattering theory, though it is not predictable by a Bragg model. Cross‐polarization backscatter intensity shows potential for discriminating the onset and duration of freeze events in a marginal ice zone, due to dominant backscatter from the snow cover adjacent to melt ponds. Preliminary results here outline the potential of covariance matrix derived polarimetric measurements for the inversion of advanced melt sea ice geophysical parameters, and provide a basis for the investigation of distributed targets in late season spaceborne polarimetric SAR scenes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".