Diurnal Scale Controls on C-Band Microwave Backscatter From Snow-Covered First-Year Sea Ice During the Transition From Late Winter to Early Melt
Bibliographic record
Abstract
Diurnal observations of coincident in situ physical-, electrical-, and surface-based C-band microwave scattering properties of a 16-cm saline snow cover on smooth, moderately saline, first-year sea ice are presented for the transition period between late winter and early melt. Statistical regression analysis and backscatter modeling are employed to explore the scattering mechanisms within the snowpack and to assess associations between backscatter and snow properties for both periods. Our results demonstrate substantial variation in both measured snow properties and C-band microwave backscatter over the diurnal cycle during the late winter period when the difference between maximum and minimum air and snow surface temperature was approximately 5 °C. No such variation in snow properties and backscatter occurred for the early melt period when our case study exhibited a small diurnal variation (~1 °C) in air and snow surface temperature. Statistical and modeled results show significant association between the microwave backscatter and snow properties for the top 8 cm of the snowpack. Basal snow properties do not contribute toward total backscatter in either case. As a result, we are certain that the sea ice surface was a negligible scattering interface during both cases. Correlations between backscatter and snow properties are incidence angle dependent, demonstrating the highest association at 50°. Diurnal backscatter from RADARSAT-2 synthetic aperture radar acquisitions support the influence of varying diurnal snow properties on C-band backscatter, showing a difference of ~4 dB for σohhand σvvoduring the late winter period. This difference is reduced to <;1 dB for the early melt period.
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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 | 0.000 | 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) | 0.000 | 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".