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Record W4229871877 · doi:10.1121/1.4799327

Comparative auditory biomechanics probed by otoacoustic emissions

2013· article· en· W4229871877 on OpenAlexaff
Christopher Bergevin, Wei Dong, Laurel H. Carney, David S. Velenovsky, Kevin E. Bonine, James L. Jarchow

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsAuditory pathwaysBiologyAudiologyNeurosciencePsychologyMedicine

Abstract

fetched live from OpenAlex

Since Kemp's discovery in 1978, otoacoustic emissions (OAEs) have provided valuable sci- entific and clinical tools for the study of the ear. For example, OAEs can provide objective measures of sensitivity and selectivity over the frequency range of 'active' hearing. Given the universality of OAEs across the kingdom Animalia, comparative studies can reveal how various morphological factors affect pe- ripheral auditory transduction and thereby what information is encoded for higher level cognition. Motivated by the complexity of cochlear mechanics and the many unknowns that currently exist, the present study de- scribes OAEs stemming from two non-mammalian groups whose auditory periphery is relatively simpler than that of mammals: several lizard genera (Heloderma, Tiliqua, Agama, and Tupinambis) that exhibit sig- nificant relative differences in tectorial membrane structure, and a highly vocal bird species (Melopsittacus undulatus). By utilizing recent improvements in OAE measurement and analysis strategies combined with quantitative anatomical measures (e.g., number of hair cells), these data shed new light upon emission gen- eration mechanisms and how such tie back to a given species' ability to encode ecologically relevant sounds. Furthermore, these data serve to inform theoretical models of auditory biophysics by clarifying what roles various morphological features do (or do not) play.

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 categoriesnone
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 score0.107
Threshold uncertainty score0.782

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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2013
Admission routes1
Has abstractyes

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