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Record W4393970841 · doi:10.1002/9781119763222.ch3

Equivalence Principle and Integral Equations in Layered Media

2024· other· en· W4393970841 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMathematics
TopicDifferential Equations and Boundary Problems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEquivalence (formal languages)MathematicsIntegral equationMathematical analysisCalculus (dental)Pure mathematicsMedicine

Abstract

fetched live from OpenAlex

Computation of the electromagnetic fields in the presence of the objects embedded in layered media requires solution of the Maxwell equations constrained with appropriate boundary conditions. Two commonly used techniques for evaluation of electromagnetic fields in multilayered structures are the integral equation method and the differential equation methods based on the finite-element and finite-difference discretization. The integral equation method relies on the availability of the layered medium Green's functions which the structure is embedded in and, thus, has ability to localize the unknown quantities to the surfaces of only the conductors and finite dielectric insertions in the layers. In this chapter the authors present detailed derivation of such integral equations formulations suitable for solution of both static and full-wave problems. They review the reciprocity relation, which is key to formulation of the equivalence principle and integral equations for the quasi-electrostatic fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.095
GPT teacher head0.362
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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