Auditory Functional Analysis: Lessons From the Primate Auditory Ossicles
Bibliographic record
Abstract
Soon after the auditory structures were first described, mechanical engineering theory was applied in order to understand the movements of the various components and the forces generated during this process, with the aim to accurately postulate the actual hearing of an individual or species. Many acoustic hypotheses are limited by their assumption of perfect conditions or ideal movements and rely on the ossicular chain functioning as a mechanical lever unit, however, changes in the angles between these bones have not been considered and until such studies are conducted, the movements of the ossicular chain under sound pressure cannot be accurately modelled. To address this issue, a landmark analysis was conducted on ultra‐high resolution computer tomography (UhrCT) scans of twenty‐six primate auditory ossicle chains. The study allowed not only visualisation but quantification of unexpected angles between bones and articulations which may answer questions regarding ossicle flexibility and relative motions. The morphometry of stand‐out specimens including the articulation angle of V.v. variegata, the relatively large chain of Daubentonia, the bulbous and shortened chain of A. calabarensis and the mediolaterally constricted bodies of T. bancanus suggest that many of the foundational mechanical theories of the auditory system will need to be revised to include more variables than they currently accept. Grant Funding Source : NSERC discovery grant to MTS and the Canadian Research Chairs program for YC
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".