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Record W4385214819 · doi:10.1038/s41598-023-39151-0

Quantitative assessment of inner ear variation in elasmobranchs

2023· article· en· W4385214819 on OpenAlexaff
Derek J. Sauer, Craig A. Radford, Christopher G. Mull, Kara E. Yopak

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsDalhousie University
FundersMarsden FundUniversity of North Carolina WilmingtonRoyal Society Te Apārangi
KeywordsInner earVariation (astronomy)BiologyAnatomy

Abstract

fetched live from OpenAlex

Considerable diversity has been documented in most sensory systems of elasmobranchs (sharks, rays, and skates); however, relatively little is known about morphological variation in the auditory system of these fishes. Using magnetic resonance imaging (MRI), the inner ear structures of 26 elasmobranchs were assessed in situ. The inner ear end organs (saccule, lagena, utricle, and macula neglecta), semi-circular canals (horizontal, anterior, and posterior), and endolymphatic duct were compared using phylogenetically-informed, multivariate analyses. Inner ear variation can be characterised by three primary axes that are influenced by diet and habitat, where piscivorous elasmobranchs have larger inner ears compared to non-piscivorous species, and reef-associated species have larger inner ears than oceanic species. Importantly, this variation may reflect differences in auditory specialisation that could be tied to the functional requirements and environmental soundscapes of different species.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.291
Teacher spread0.276 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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