Analyses of c-band microwave backscatter to wind-roughened first-year sea ice melt ponds
Bibliographic record
Abstract
Variations in wind forcing over summer first-year sea ice (FYI) melt ponds occur at hourly to weekly scales and are a significant contributor to backscatter (/spl sigma//spl deg/) variability observed from space borne synthetic aperture radar (SAR) platforms (e.g., ENVISAT-ASAR and RADARSAT-1). This variability impairs our ability to use SAR to derive information on summer sea ice thermodynamic state and energy balance parameters such as albedo and melt pond fraction. The surface roughness contribution to like-polarized, C-band /spl sigma//spl deg/ estimates from FYI melt ponds in the Canadian Arctic is addressed through a spectral and statistical analysis of surface wave height profiles for varying wind speeds, upwind fetch lengths, and melt pond depths. Our hypothesis is that the perturbation of wind-roughened melt pond surfaces to wavelengths that are resonant to C-band SARs is driven by upwind fetch and wind speed. Significant scale surface roughness was observed even at wind speeds of 3 m s/sup -1/ resulting in /spl sigma//spl deg/ (HH) ranging from -6 dB at 20/spl deg/ incidence to -24 dB at 50/spl deg/ incidence. Results from a multivariate linear regression analysis show that 53.4% of observed variance in /spl sigma//spl deg/ (HH or VV) can be explained by wind speed (0.9 m height), upwind fetch from melt pond edges, and depth of melt ponds, with no appreciable difference in the relative contribution of those explanatory variables.
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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 | 0.000 |
| 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".