Sea Ice Detection Based on Unambiguous Retrieval of Scattering Coefficient from GNSS-R Delay-Doppler Maps
Bibliographic record
Abstract
In this paper, a method is proposed for detecting sea ice based on the scattering coefficient (σ0) retrieved from Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDMs). First, a strategy for unambiguously retrieving the surface scattering coefficients is illustrated, by employing the spatial integration approach (SIA) and the multi-scan technique. Next, the application of this proposed scheme for sea ice remote sensing is demonstrated using DDMs acquired from the U.K. TechDemoSat-l (TDS-l) mission and verified with the collocated reference sea ice concentration (SIC) data obtained by Nimbus-7 SMMR and DMSP SSM/I-SSMIS sensors. The test results show that the proposed method offers a new solution to sea ice detection and a feasible aspect for resolving the ambiguity in the retrieval of σ0from GNSS-R DDMs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".