Low microwave brightness temperatures in central Antarctica: observed features and implications
Bibliographic record
Abstract
Abstract The microwave brightness temperature of the ice sheet in Antarctica has a few narrow, elongated features of very low microwave brightness temperatures. The contrast inside and outside of these areas is at least 10 K at 37 GHz. These features are persistent throughout the year and from year to year and occur at 6.6,10.7,18 and 37 GHz. The exceptionally large frequency gradients observed in this narrow area suggest that very large snow grains exist in the snow cover. Past traverse data revealed that the entire surrounding region undergoes large temperature gradients that drive high sublimation causing very large snow grains to form and the formation of both surface and depth hoar. A detailed study of former snow pits showed that, at the place of the unique features, the snow grains from the surface down to the first 2 m are the largest observed over the region. A digital elevation model shows the surface is nearly flat, and corresponds to a subglacial depression in the underlying bed. Previously reported kilometer-scale snow dunes are observed up- and downslope of the level area, but not within the level area, where the brightness temperature is lowest. In this katabatic zone, the drift-snow redistribution and the natural wind-shadow induced by the slope depression create a protected area where the snow grains can grow into very large grains.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".