Bibliographic record
Abstract
Spontaneous otoacoustic emissions (SOAEs) have become a hallmark feature in modern theories of an 'active' inner ear, given their numerous correlations to auditory function (e.g., threshold microstructure, neurophysiological tuning curves), near universality across tetrapod classes, and physiological correlates at the single hair cell level.However, while several different classes of nonlinear models exist that describe the mechanisms underlying SOAE generation (e.g., coupled limit-cycle oscillators, global standing waves), there is still disagreement as to precisely which biophysical concepts are at work.Such is further compounded by the idiosyncratic nature of SOAEs: Not all ears emit, and when present, SOAE activity can occur at seemingly arbitrary frequencies (though always within the most sensitive range of the audiogram) and in several forms (e.g., peaks, broad 'baseline' plateaus).The goal of the present study was to develop new signal processing and stimulation techniques that would allow for novel features of SOAE activity to be revealed.To this end, we analyzed data from a variety of different species: human, lizard, and owl.First, we explored several strategies for examining SOAE waveforms in the absence of external stimuli to further ascertain what constitutes 'self-sustained sinusoids' versus 'filtered noise'.We found that seemingly similar peaks in the spectral domain could exhibit key differences in the time domain, which we interpret as providing critical information about the underlying oscillators and their coupling.Second, we introduced dynamic stimuli (swept-tones, tone bursts) at a range of levels, whose interaction with SOAEs could be visualized in the time-frequency domain.Aside from offering a readily accessible way to visualize many previously reported effects (e.g., entrainment, facilitation), we observed several new features such as subharmonic distortion generation and competing pulling/pushing effects when multiple tones were present.Furthermore, the tone burst data provide quantitative bounds on the dynamics of the relaxation oscillations.These data should provide new insights into how precisely how SOAE generators are related to (the more commonly measured) OAEs evoked via external stimuli and presumably speak to the robustness of the hair cell as the underlying basis for SOAE activity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".