Estimation of Ice Concentration from Sar Using Multiscale Ice and Water Retrievals
Bibliographic record
Abstract
In this study, we present a new technique for automated retrieval of ice concentration from RADARSAT-2 dual-polarization HH-HV ScanSAR images. We extended our previously introduced ice and water detection approach (based on more than 15,000 SAR images) operating at 2.05 km x 2.05 km spatial scale to a set of 19 retrieval scales ranging from 2.05 km (41 pixel) down to 0.25 km (5 pixel). Then we designed a technique for estimating ice concentration in 2 km x 2 km (40 x 40 pixel) areas using ice and water retrievals derived at multiple spatial scales. We demonstrated that the proposed approach shows a very good agreement with the Canadian Ice Service (CIS) Image Analysis ice concentrations (RMSE=2.2%, R2 = 0.996). The developed technique will be adapted to the data stream from the RADARSAT Constellation Mission (RCM) for data assimilation in Environment and Climate Change Canada (ECCC) Regional Ice-Ocean Prediction System (RIOPS).
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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 | 0.000 |
| 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".