ERS SAR studies of sea ice signatures in the Pechora Sea and Kara Sea region
Bibliographic record
Abstract
Results from a statistical analysis of 105 synthetic aperture radar (SAR) images and meteorological data are presented, covering parts of the Pechora Sea and Kara Sea in the Russian Arctic. Wind speed, air temperature, and other data were collected for the SAR sample areas, and a manual sea ice classification of the SAR samples was performed. All variables were input to different multivariate regression analyses, which were used to separate ice and water samples. In subsequent regression analyses the classes young ice and rough first-year ice were separated from water, and the two ice types were separated from one another. In the separation of all ice types from water, correlation coefficients of up to 0.90 were achieved between predicted and actual values. Correlation coefficients were as high as 0.93 between predicted and actual values for open water and young ice and for open water and rough first-year ice. When trying to separate young ice from rough first-year ice, correlation coefficients of about 0.60 were obtained. The study indicated that the mean and standard deviation of the backscattering coefficients and air temperatures were the most important information for separating water and sea ice using regression techniques.
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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 | 0.000 | 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 | 0.000 |
| 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".