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Record W1583981997

Spatial vibration patterns of the gerbil eardrum

2007· article· en· W1583981997 on OpenAlexafffund
Nicolas N. Ellaham, Fadi Akache, W. Robert J. Funnell, Sam J. Daniel

Bibliographic record

VenueeScholarship@McGill (McGill) · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMcGill University
FundersStanford Maternal and Child Health Research InstituteCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsEardrumAcousticsVibrationMalleusDisplacement (psychology)Laser Doppler vibrometerPhysicsPerpendicularOpticsRotation (mathematics)Materials scienceMiddle earMathematicsGeometryWavelengthAnatomy
DOInot available

Abstract

fetched live from OpenAlex

Laser Doppler Vibrometry (LDV) displacement measurements for the better understanding of middle-ear mechanics in mammals were carried out on Mongolian gerbils to measure the velocity of a vibrating surface at the nanometer level without mass loading. Displacements were measured at multiple points along the manubrium of the malleus and along a line on the eardrum perpendicular to the manubrium, in order to study the spatial vibration patterns. The spatial vibration patterns across the eardrum and along the manubrium were analyzed over the frequency range form 0.15 to 10 KHz. The similarity of the shapes of the frequency responses at all points of measurements indicates that the motion of the gerbil eardrum follows a simple pattern, with all points vibrating in phase. The manubrial displacements observed are consistent with the traditional concept of a simple rotation around a fixed axis extending from the anterior mallear ligament to the posterior includal ligament.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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 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

Citations2
Published2007
Admission routes2
Has abstractyes

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