Investigation of the sensitivity response of Touzi target scattering decomposition to modeled early ice growth
Bibliographic record
Abstract
Touzi's scattering vector model allows for a unified decomposition of both coherent and incoherent target scattering.Based on this model, a unique and roll-invariant model can be developed for target decomposition.Our study aims at the investigation of the Touzi decomposition for lake and sea ice monitoring.Thus, we investigate the sensitivity of the target parameters obtained from the Touzi incoherent decomposition to modeled ice growth.Our study focuses on thermodynamically-grown fast lake and sea ice during the early ice growth.A time-series quad pol synthetic aperture radar (SAR) imagery was acquired over a study site around the Resolute Bay area.Results indicate that for lake ice, the scattering type magnitude (α s ) decreases within a thickness up to 20 cm.This means switching from volume scattering to dominant surface scattering.The same sensitivity is observed for young sea ice with thickness up to 30 cm, which corresponds to modeled ice bulk salinity of 8.5‰.Remarkably, we found a trend of increasing target orientation angle (ψ) for thin sea ice up to 20 cm, which corresponds to a bulk salinity of 10.3‰.This trend is unique to the case of sea ice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".