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Record W4404217616 · doi:10.1101/2024.11.09.24317036

FREQUENCY SPECIFICITY OF NARROWBAND CHIRP AND 2-1-2 STIMULI: SPECTRAL ANALYSES

2024· preprint· en· W4404217616 on OpenAlexafffund
Ronald Adjekum, Susan A. Small, David R. Stapells

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNarrowbandComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

Abstract Objective The current study examined the frequency specificity of NB chirps by comparing the spectral characteristics of 500-, 1000-, 2000- and 4000-Hz NB CE-Chirp ® LS stimuli with those of 2-1-2 tones. Design Spectral characteristics including the centre frequency, bandwidth, and stimulus energy changes after stopband filtering were compared. The bandwidth was computed as the difference between the upper and lower frequencies at -20 dB (& -3 dB) cutoff points of the main lobe; the centre frequency was determined as the geometric mean of the upper and lower frequencies at the -20 dB (& -3 dB) cutoff points. Results At 100 dB peSPL, the bandwidths of the 500-, 1000-, and 2000-Hz NB CE-Chirp ® LS acoustic spectra were 1.7-2.5 times wider than the acoustic spectra for the 2-1-2 tones; the 4000-Hz NB CE-Chirp ® LS bandwidths were 1.4-1.6 times wider than those of the 2-1-2 tones. The energy of NB CE-Chirp® LS stimuli was concentrated within ±0.75 octave of the centre frequency, compared to ±0.5 octave for 2-1-2 tones. Conclusion NB CE-Chirp ® LS stimuli demonstrated poorer frequency specificity compared with 2-1-2 tones. Further studies are needed to investigate the place specificity of the ABRs to NB CE-Chirp ® LS before implementing them clinically.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.296
Teacher spread0.252 · 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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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