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Record W2113047383

Did you say "bionic" ear ?

2014· article· en· W2113047383 on OpenAlexaffvenue
Jérémie Voix

Bibliographic record

VenueCanadian acoustics · 2014
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEar canalEngineeringWirelessTelecommunicationsMiniaturizationAcousticsComputer scienceElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Over the past decades, Hearing Protection Devices have existed simply as passive acoustical barriers intended to prevent sound from reaching the ear canal. Over the last decade though, with the increasing miniaturization of electronic components and consolidation of consumer electronic goods, new electronic Hearing Protection Devices have been brought on the marketplace to protect from noise induced hearing loss in more sophisticated ways. Likewise, Hearing Aids have benefited from this sophistication and entirely new communication devices have been developed, such as the wireless cellphone earpiece. The convergence of hearing protection devices, hearing aids and communication earpieces appears to be the next step and is sometimes referred to as a ear. This presentation will detail a possible roadmap leading to the development of this bionic technology. It will also present several other intra-aural applications ranging from in-ear energy harvesting, to hearing-health monitoring and brain-wave recording, all of which could truly make your next earpiece a ear.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0380.018

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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2014
Admission routes2
Has abstractyes

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