Snow and sea ice roughness characterization from Quad-Pol H-A-α classes relative distribution
Bibliographic record
Abstract
At spring when solar irradiance and air temperature cause snow and ice into an array of surface scatterers, point scatterers become the main signature of ridged environment. This information is better retained in unfiltered T3 matrixes. On an other hand Lee filtered T3 matrix gives information about the diversity of a distributed target which is equally indicative of the higher variability within ridges. In order to keep both in a single set of data, we computed an hybrid matrix composed of the alpha parameter from a single look T3 matrix and the entropy of the same matrix after a Lee 3×3 matrix. As revealed running a Wishart classification algorithm, this approach increases greatly contrast between ridged and flat areas. While this couldn't be used during most o the winter season, this may present a great potential to extract narrow linear structures when snow wetness increase surface scattering and therefore single and double bounce scattering mechanisms.
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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) | 0.002 | 0.001 |
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".