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Record W2593753107 · doi:10.1155/2017/2134868

Terahertz Spectroscopy and Brewster Angle Reflection Imaging of Acoustic Tiles

2017· article· en· W2593753107 on OpenAlexafffundabout
Patrick Kilcullen, Mark R. A. Shegelski, MengXing Na, David N. Purschke, Frank A. Hegmann, M. Reid

Bibliographic record

VenueJournal of Spectroscopy · 2017
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of AlbertaUniversity of Northern British Columbia
FundersDefence Research and Development CanadaAlberta Innovates - Technology FuturesUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaUniversity of Northern British ColumbiaMcGill University
KeywordsTerahertz radiationBrewster's angleOpticsBrewsterMaterials scienceTileReflection (computer programming)Terahertz spectroscopy and technologyRefractive indexBroadbandOptoelectronicsPhysicsComputer science

Abstract

fetched live from OpenAlex

A Brewster angle reflection imaging apparatus is demonstrated which is capable of detecting hidden water-filled voids in a rubber tile sample. This imaging application simulates a real-world hull inspection problem for Royal Canadian Navy Victoria-class submarines. The tile samples represent a challenging imaging application due to their large refractive index and absorption coefficient. With a rubber transmission window at approximately 80 GHz, terahertz (THz) sensing methods have shown promise for probing these structures in the laboratory. Operating at Brewster’s angle allows for the typically strong front surface reflection to be minimized while also conveniently making the method insensitive to air-filled voids. Using a broadband THz time-domain waveform imaging system (THz-TDS), we demonstrate satisfactory imaging and detection of water-filled voids without complicated signal processing. Optical properties of the tile samples at low THz frequencies are also reported.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.271
Teacher spread0.262 · 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 designObservational
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

Citations7
Published2017
Admission routes3
Has abstractyes

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