Impact of Daily Melt and Freeze Patterns on Sea Ice Large Scale Roughness Features Extraction
Bibliographic record
Abstract
Results presented in section 4 and 5 underline the critical impact of wetness changes during spring time. With snow temperatures at the freezing point, any change in the snowatmosphere heat flux initiates melt or freezing in the top layer of the snowpack. While, within the snowpack, water content undergoes only slight variations over a 24 hours period, snow crystals within the top layer can turn from wet to dry overnight. More importantly, where an ice surface is exposed to the atmosphere, melt and freezing cause the cyclic development of a liquid film and a re-crystallized highly diffusive dry layer of snow, ice and air bubbles. As liquid water is removed, part of the microwave energy penetrates into this layer to be absorbed and scattered. The fact that the ridge network can completely vanish overnight emphasizes the importance of specular reflection on electro-magnetically smooth surfaces for the extraction of ridges. Surface roughness changes not only increase (or decrease) drastically the back-scattering from surfaces oriented perpendicularly to the SAR beam but also cause the back-scattered electromagnetic wave to be the result of an incoherent (or noncoherent) or coherent reflection. To explore furthermore the implications we need to clarify the concept of incoherence. In principle we measure the combined reflections from all scatterers within the scene which in term of physics is coherent summation. However, incoherence, in the literature, refers to the nature of the scene rather than to the physics of electro-magnetic waves. If the scene is viewed twice through the exact same geometry, the same speckle pattern should be observed (Raney, 1998). Conversely, due to the random distribution of individual scatterers, a slight change in the position of the satellite would give a totally different speckle pattern despite the fact that the statistics over the scene would remain unchanged. This is the incoherence nature of speckle which turns a mostly uniform area into a salt an pepper image. On the other hand, coherent specular reflection describes a highly correlated phase structure within part of a scene, which implies that multiple measurements from this same scene would reveal the presence of a specular reflector. This requires that the satellite position remains the same in order for the relative geometry of the SAR beam angle and target to be about the same. Polarimetric SAR are systems that acquire two simultaneous images (HH and VV) of a same scene. While the geometry of the relative positions of the satellite and scene are unchanged, the physics of coherent summation from multiple scatterers implies that the two images would show a different speckle pattern. Conversely, the nature of specular coherent reflection implies that a specular reflector would be pinpointed on both images. It follows that the cross-correlation of single look HH and VV channels could help filter bright pixels generated by speckled coherent summation while revealing the pressure ridge network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".