Multi-frequency microwave backscatter indices from saline snow covers on smooth first-year sea ice
Bibliographic record
Abstract
This study inter-compares observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A surface-borne multi-frequency (Ku-, X- and C-bands) polarimetric microwave scatterometer system is used near-coincident with in situ snow geophysical measurements. The study investigated differences in scatterometer observations for all three frequencies, co-pol ratios, and introduced new dual-frequency ratios to discriminate dominant polarization-dependent frequencies from these snow covers. Preliminary results suggest that, thinnest 4 cm snow cover demonstrate greatest increase in microwave backscatter from all three frequencies, followed by backscatter from thicker 8 cm and 14 cm snow covers. Dual-frequency indices derived for all frequency and polarization combinations suggest greater sensitivity of Ku-band microwaves to snow grain microstructure with increasing snow thicknesses, X-band microwaves to changes in snow salinities with decreasing snow thicknesses. Our results indicate the effect of dielectric loss associated with high salinities throughout all layers of the three snow covers, as the dominant factor affecting microwave penetration and backscatter from all three frequencies.
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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) | 0.000 | 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".