The notion of “frequency clusters” in spontaneous otoacoustic emission generation
Bibliographic record
Abstract
Normal, healthy ears can emit unprovoked low-level sounds called spontaneous otoacoustic emissions (SOAEs). These arise in a wide variety of different species (e.g., humans, lizards) despite gross morphological differences commonly thought important to the associated biomechanics. As such, theoretical models of SOAE generation differ significantly in their underlying assumptions. One model class [Vilfan and Duke (2008), Biophys. J. 95, 4622–4630; Wit & van Dijk, 2012 JASA 132, 918–926] uses a coupled nonlinear oscillator framework, where each element exhibits a limit cycle (with a unique characteristic frequency) and is visco-elastically coupled to its nearest neighbor. This model proposes that SOAEs arise by groups of oscillators that self-entrain into “clusters,” defined simply as the frequency where the largest peak in the steady-state spectral magnitude occurs. Our study sought to computationally explore more precisely what constitutes a cluster in terms of the underlying oscillator's dynamics. We found that oscillators within a cluster exhibit relatively complicated motions and poor phase coherence. Coupled with the model's inability to reproduce realistic SOAE spectra, the biomechanical relevance of a “cluster” is called into question. Modifications to the model for lizard ears (e.g., universal coupling via the rigid basilar membrane; Bergevin & Shera (2010), JASA 2398–2409) are explored.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".