A Study on the C-Band Polarimetric Scattering and Physical Characteristics of Frost Flowers on Experimental Sea Ice
Bibliographic record
Abstract
A focused study on the C-band polarimetric scattering and physical characteristics of frost-flower-covered sea ice was conducted at the Sea-Ice Environmental Research Facility over a three day period. Sea ice was grown in an outdoor pool outfitted with automated sensors to monitor environmental conditions. C-band polarimetric scattering measurements were conducted continuously at a range of incidence angles, and surface roughness statistics were obtained at discrete times using a laser scanner system LiDAR. Four stages of development were identified that exhibited notably different physical and scattering characteristics: 1) initial formation; 2) surface brine expulsion; 3) frost flower growth; and 4) decimation. An optimal polarization and incidence angle is not readily apparent for the purposes of identifying the frost flower development Stages I-III; however, the lower incidence angles (25° and 35°) appear to be most sensitive to the surface brine expulsion. Only the dual-polarization measurements at low incidence angles (e.g., 25°) could be used to identify the onset of the decimation stage. Backscatter increased rapidly during the initial formation, with a local maximum corresponding to ~ 80% areal coverage of frost flowers, followed by a local minimum when the surface was covered by a brine-rich surface layer, connoting that surface brine expulsion may be identified using polarimetric scatterometry.
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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".