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Record W2164175780 · doi:10.1098/rsbl.2013.0104

Evidence for hearing loss in amblyopsid cavefishes

2013· article· en· W2164175780 on OpenAlexaff
Matthew L. Niemiller, Dennis M. Higgs, Daphne Soares

Bibliographic record

VenueBiology Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCaveBiologyAudiogramAmbient noise levelHearing lossHabitatSTREAMSRange (aeronautics)Noise (video)EcologyAudiologyAcousticsSound (geography)

Abstract

fetched live from OpenAlex

The constant darkness of caves and other subterranean habitats imposes sensory constraints that offer a unique opportunity to examine evolution of sensory modalities. Hearing in cavefishes has not been well explored, and here we show that cavefishes in the family Amblyopsidae are not only blind but have also lost a significant portion of their hearing range. Our results showed that cave and surface amblyopsids shared the same audiogram profile at low frequencies but only surface amblyopsids were able to hear frequencies higher than 800 Hz and up to 2 kHz. We measured ambient noise in aquatic cave and surface habitats and found high intensity peaks near 1 kHz for streams underground, suggesting no adaptive advantage in hearing in those frequencies. In addition, cave amblyopsids had lower hair cell densities compared with their surface relative. These traits may have evolved in response to the loud high-frequency background noise found in subterranean pools and streams. This study represents the first report of auditory regression in a subterranean organism.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.245
Teacher spread0.094 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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