Melt ponds on sea ice in the Canadian Archipelago: 2. On the use of RADARSAT‐1 synthetic aperture radar for geophysical inversion
Bibliographic record
Abstract
Microwave scattering from a first‐year sea ice (FYI) melt ponded surface is examined using RADARSAT‐1 synthetic aperture radar (SAR) data collected during the 1997 Collaborative‐Interdisciplinary Cryospheric Experiment (C‐ICE'97) near Resolute Bay, Nunavut. This paper (1) investigates the utility of time series of microwave scattering to detect melt pond formation and (2) investigates approaches toward geophysically inverting information on the physical and radiative properties of this surface. We found melt pond formation to coincide with a sharp rise in the temporal evolution of the microwave scattering coefficient (σ°) over FYI. RADARSAT‐1 incidence angle and surface wind speed explained >90% of the variation in σ°. RADARSAT‐1 σ° was sensitive (R2 = 0.80) to the fractional coverage of melt ponds during windy conditions (∼ 5.3 m s−1). Spatial and temporal coincident measurements of RADARSAT‐1 σ° and the integrated shortwave albedo revealed a strong negative statistical correlation (R2 = 0.91) during windy conditions (∼ 5.3 m s−1). A weaker, but strong, negative relationship (R2 = 0.78) was observed for less windy conditions (∼ 3.2 m s−1), and a very weak positive relationship (R2 = 0.19) was found for low wind speed conditions (∼ 1.5 m s−1). All relationships were observed for melt pond fractions between 13 and 34%.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".