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Record W2009781857 · doi:10.1139/p07-006

Buried object approach for solving scattering problems related to rough surfaces

2007· article· en· W2009781857 on OpenAlexvenueno aff
Yasemin Altuncu, Ali Yapar, İbrahim Akduman

Bibliographic record

VenueCanadian Journal of Physics · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsScatteringPlanarIntegral equationMethod of moments (probability theory)Mathematical analysisBoundary (topology)Boundary value problemElectromagnetic radiationCross section (physics)Fredholm integral equationInterface (matter)Surface (topology)OpticsGeometryQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

A new method for the scattering of electromagnetic waves from a locally rough interface between two dielectric half-spaces is addressed. The method is based on the assumption that the perturbations of the rough surface from the planar interface are objects buried in a two-half spaces media with a planar boundary, which allows one to reduce the problem to the scattering of electromagnetic waves by cylindrical bodies of arbitrary cross section. Then through the Green's function of the background medium one obtains a Fredholm integral equation of the second kind, which is solved via an application of the Method of Moments. The present formulation permits one to get the near and far field expressions of the scattered wave. The method is effective for surfaces having a local roughness and arbitrary root mean square heights but does not work in the case of a highly conductive background medium.PACS Nos.: 41.20.–q, 41.20.Jb, 03.50.De

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2007
Admission routes1
Has abstractyes

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