WINTER SEA ICE MAPPING FROM MULTI-PARAMETER SYNTHETIC APERTURE RA DAR DATA Eric and
Bibliographic record
Abstract
this paper, we evaluate on a quantitative basis the added value of various frequencies and polarizations for mapping icc conditions for estimating ice fractions. Ice maps derived from multi-parameter SAR data validated using a combination of ancillary information arc used yardstick to predict the level of performance of current and future single-frequency single-polarization satellite systems for winter ice mapping such as - 1 SA the Japanese Earth Satellite SAR and others, 1991), and the Canadian SAR and others, 1991), and possible combined information from these active sensors. identified winter ice conditions arc: 1 ) (MY), 2) compressed first year sea-ice 3) first-year rubble icc ridges 4) first-year rough ice 5) first-year smooth icc and thin first-year Although open water is absent from all data, it is included in the discussion as a possible icc type. 2. AIRSAR DATA SET acquired SAR observations of sea-ice in 1988 over the Sea, 3 of Alaska, C- = I,- 1'- = band frequency, the scattering matrix (van 1981; van and 1990) of resolution is recorded, and the data arc processed such the scattering matrices at different frequencies are spatially registered. A matrix comprises 4 complex numbers S VII , where is a complex number representation of the amplitude and phase of the radar return received V- (vertical) polarization when (horizontal) polarization is transmitted. Subsequent SAR processing yields data in Stokes format and 1990), or equivalently in cross-product format, i.e. for each pixel the cross-products < > are stored, where X, Y, X', and Y' arc H or V's, an asterisk denotes complex conjugation, and < > indicates a spatial averaging process over several (4 in practice) contiguous elements called processing in the literature. Although there 16 cross-products for...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".