A real-time implementation of the method of principal components applied to dual-polarized radar returns
Bibliographic record
Abstract
Summary form only given. Experiments performed with dual-polarized Ku-band radar systems have shown that there are distinct differences between the information contained in the like- and cross-polarized returns from the ice floes, particularly between those returns from new and old ice. In order to present the two different images on one monochrome display, it is necessary to combine them. The process can be expedited by using singular-value decomposition (SVD) to determine the eigenvectors, since, in doing so, it is not necessary to compute the covariance matrix explicitly. For the special case of transforming two input images into one output image, the SVD can be computed in a straightforward manner using the rotation matrix of Hestenes (1958). By performing the image transformation using parallel processors, an efficient pipelined architecture for computing the method of principal components can be realized. Such an architecture has been simulated on the Warp systolic computer and applied to the like- and cross-polarized radar images.>
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".