Passive microwave remote sensing of seasonal snow-covered sea ice
Bibliographic record
Abstract
The Arctic is thought to be an area where we can expect to see the first and strongest signs of global-scale climate variability and change. We have already begun to see a reduction in: (1) the aerial extent of sea ice at about 3% per decade and (2) ice thickness at about 40%. At the current rate of reduction we can expect a seasonally ice-free Arctic by midway through this century given the current changes in thermodynamic processes controlling sea-ice freeze-up and decay. Many of the factors governing the thermodynamic processes of sea ice are strongly tied to the presence and geophysical state of snow on sea ice, yet snow on sea ice remains poorly studied. In this review, we provide a summary of the current state of knowledge pertaining to the geophysical, thermodynamic and dielectric properties of snow on sea ice. We first give a detailed description of snow thermophysical properties such as thermal conductivity, diffusivity and specific heat and how snow geophysical/electrical properties and the seasonal surface energy balance affect them. We also review the different microwave emission and scattering mechanisms associated with snow-covered first-year sea ice. Finally, we discuss the annual evolution of the Arctic system through snow thermodynamic (heat/mass transfer, metamorphism) and aeolian processes, with linkages to microwave remote sensing that have yet to be defined from an annual perspective in the Arctic.
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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 | 0.001 |
| 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".