On the semi-orthogonal wavelet matrix transform approach for the solution of integral equations
Bibliographic record
Abstract
In this paper, the semi-orthogonal wavelet (SOW) and Daubechies orthogonal wavelet (DOW) transforms have been applied to the solution of integral equations arising from the scattering by a conducting cylinder excited by a TM plane wave, and the effect of thresholding on the matrix sparsity and the solution accuracy has been examined for the entire practical range of matrix sparsity. Numerical results show that, although the computational costs of the SOW and the DOW matrix transformations are practically the same, the SOW matrix transform approach yields a highly sparse MoM matrix with a much smaller threshold value, and gives a better solution accuracy than the DOW matrix transform approach when the matrix sparsity is less than a certain value. However, the relative error /spl epsiv/ increases irregularly with the increase of the matrix sparsity when using the SOW, which indicates a worse transform matrix condition number in this case.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".