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Record W4317809320 · doi:10.1080/17455030.2023.2169387

Close link between Fabry–Pérot resonance and natural-resonance frequencies

2023· article· en· W4317809320 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

VenueWaves in Random and Complex Media · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResonance (particle physics)Fabry–Pérot interferometerDispersion (optics)Reflection (computer programming)Natural frequencyOpticsPermittivityDielectricPhysicsMaterials scienceNuclear magnetic resonanceAtomic physicsAcousticsWavelength

Abstract

fetched live from OpenAlex

Mathematical and numerical analyses have been performed to examine the close link between Fabry–Pérot resonance and natural-resonance frequencies. For the mathematical analysis, the conditions resulting in minimum magnitudes of reflection coefficients in frequency-domain are derived for air-backed and metal-backed low-loss non-dispersive (or weakly dispersive) dielectric samples with relative complex permittivity εr and thickness L for free-space wave propagation at normal incidence. The close relation between Fabry–Pérot resonance and natural-resonance frequencies is demonstrated for three different sample scenarios as (i) no-dispersion and lossless case (εr=4.8−j0.0 and L = 50 mm), (ii) no-dispersion and low-loss case (εr=4.8−j0.01 and L = 50 mm), and (iii) weak-dispersion and low-loss case (εr=4.8−jσ/(ωε0) and L = 50 mm where σ is the conductivity of the sample and ω is the angular frequency). It is noted that operating frequency should be increased to observe late-time natural-resonance frequencies for a sample with smaller length or vice versa.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.247
Teacher spread0.217 · 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