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Record W1978483721 · doi:10.1121/1.4742729

Slow and fast ultrasonic wave detection improvement in human trabecular bones using Golay code modulation

2012· article· en· W1978483721 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

VenueThe Journal of the Acoustical Society of America · 2012
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsCanadian Association for Co-operative EducationUniversity of Toronto
Fundersnot available
KeywordsBinary Golay codeChirpAcousticsModulation (music)AttenuationNoise (video)Sine waveOpticsSIGNAL (programming language)Computer sciencePhysicsMaterials scienceAlgorithmImage (mathematics)Computer vision

Abstract

fetched live from OpenAlex

The identification of fast and slow waves propagating through trabecular bone is a challenging task due to temporal wave overlap combined with the high attenuation of the fast wave in the presence of noise. However, it can provide valuable information about bone integrity and become a means for monitoring osteoporosis. The objective of this work is to apply different coded excitation methods for this purpose. The results for single-sine cycle pulse, Golay code, and chirp excitations are compared. It is shown that Golay code is superior to the other techniques due to its signal enhancement while exhibiting excellent resolution without the ambiguity of sidelobes.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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