Diurnal SAR variability due to ice and snow air interface wetness overnight changes.
Bibliographic record
Abstract
For a water content over 8%, the transmitted component of a SAR beam becomes negligible. The electromagnetic wave interaction with the interface discontinuity is then determined by the scattering efficiency of surface roughness rms and correlation height. It is illustrated by the difference of contrast observed on images acquired from a same scene one late in the afternoon after a warm day and the development of a wet interface, and the other, early in the morning after a cool night and growth of frost flower in place of the wet interface. The increased the rms height of newly formed crystals reduce the angle dependence of the backscattered signal and therefore the contrast on the scene, however, the increased volume scattering in snow covered areas seems to be the main factor causing a resolution difference on the ridge network between images recorded on morning and at the end of the afternoon. A detailed survey of the air-snow or air-ice interface of the snow cover and ice blocks surfaces. Surface wetness, snow wetness content and surface roughness are documented. Radar data measured on a variety of snow and ice interfaces are presented and analyzed. We show that the presence of a wet film on snow and ice surfaces produces an increased forward scattering whereas interfaces covered with newly crystallized ice display an enhanced backscattering.
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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) | 0.001 | 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".