Convergent otoacoustic tuning estimates in the anole lizard
Bibliographic record
Abstract
Spontaneous otoacoustic emissions (SOAEs) are one hallmark of an active ear and provide insight into the underlying biomechanics. SOAEs interact readily with external tones and correlate with stimulus-frequency emissions (SFOAEs) evoked by low-level stimuli (≤ 40 dB SPL). Such findings are consistent with a canonical model of emission gen-eration based upon the principle of coherent reflection, which predicts relationships between OAE-derived tuning estimates. However, inconsistencies persist and require additional research, such as the incompatible tuning estimates between SOAE suppression-tuning curves (STCs) and SFOAE phase-gradient delays (Ns f ). Further complications stem from the nonlinear, stimulus level-dependent nature of SFOAE delays. To reconcile these uncertainties, we examined correlations between SOAE and SFOAE tuning estimates across in the green anole lizard (Anolis caroli-nensis). Specifically, we characterize the level-dependence of Ns f across frequencies using swept-tone stimuli while extracting SOAE "interaction" tuning curves (ITCs) measured simultaneously. This technique allows us to quantify both Ns f and Q values of the ITCs from the same ear using the same stimuli. Additionally, we used SOAE inter-peak spacing to calculate NSOAE to provide a stimulus-independent metric for assessing these estimates. Preliminary results suggest that it is possible to obtain convergent otoacoustic tuning estimates from the same data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".