MétaCan
Menu
Back to cohort
Record W4240281573 · doi:10.1002/047134608x.w1210.pub2

Backscattering of Plane Waves from Conducting Objects in Random Media

2018· other· en· W4240281573 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley Encyclopedia of Electrical and Electronics Engineering · 2018
Typeother
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsLakehead University
Fundersnot available
KeywordsRandomnessPlane waveCurvatureScatteringBackscatter (email)OpticsPhysicsSpecular reflectionRadar cross-sectionFocus (optics)Plane (geometry)Electromagnetic radiationRegular polygonComputational physicsGeometryMathematicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Over the last two decades, accurate methods to calculate the intensity of waves backscattering from conducting objects with arbitrary shape in random media were developed successfully. This allowed researchers to analyze numerically the radar cross section (RCS) considering different parameters, such as the configuration of the object and the effects of medium randomness. Data backscattering from the smooth surface of a conducting concave–convex target reveals the obvious impact of its configuration, including size and curvature index on the behavior of RCS. This requires the investigation of the specular reflections, especially in the case of relatively complex contours. We assume wave propagation and scattering from targets in free space and random medium, while considering linear and circular polarizations of incident waves. In this article, we review the backscattering problem and primarily focus on the plane wave as an ideal incident wave in the far field.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.187
Teacher spread0.182 · 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