Characteristic Basis Function Method for the Analysis of Composite Objects Embedded in Layered Media
Bibliographic record
Abstract
In this paper, the Poggio-Miller-Chang-Harrington-Wu-Tsai Equations associated with the Mixed Potential Integral Equations (PMCHWT-MPIE) are used to analyze objects comprising of lossy composite materials embedded in layered media, and the Discrete Complex Image Method (DCIM) is used to generate the Dyadic Green's Functions. Compared to the MOM analysis based on the Impedance Boundary Condition (IBC), the PMCHWT-based analysis is more robust when dealing with highly lossy objects embedded in layered media. We choose the Characteristic Basis Functions Method (CBFM) for this problem because it is iteration-free and, hence, is suitable for dealing with multiple RHS efficiently. Yet another reason for this choice is that the GPU can be used to accelerate the filling process of the submatrices of the impedance matrix when generating the Characteristic Basis Functions (CBFs).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".