Multi-frequency polarimetric microwave observations of snow cover on first-year Arctic sea ice
Bibliographic record
Abstract
This study explores the potential of a multi-frequency (Ku-, X- and C-band) scatterometry approach, to understand microwave interactions between teo statistically different snow thickness covers (14cm and 8cm) on first-year Arctic sea ice during the late winter to early-melt season transition. The results show substantial differences in backscatter response from all three frequencies, for both snow covers. Highly-saline snow covers with fluctuating snow geophysical and thermodynamic properties cause these backscatter fluctuations, with contributions from surface and volume scattering from different snow layers and interfaces. C-band exhibited drastic variations in backscatter, especially for the 14cm snow cover, when compared to Ku- and X-band. In the case of 8cm snow cover, all the three frequencies show minimal sensitivity to snow electro-thermo-physical properties. Our results show distinctly different snow thermodynamic processes operating within the different snow layers, essential for snow thickness estimation on first-year sea ice using active microwave remote sensing approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".