Mechano-acoustical measurement and modeling of the outer and middle ear
Bibliographic record
Abstract
Mechano-acoustical measurement and modeling have evolved together. Most early measurements of the behavior of the outer and middle ear produced either spatial averages or single-point observations, which were amenable to modeling with uniform transmission lines and lumped circuits. A major step forward was the measurement of displacement patterns on the eardrum, which called for the use of finite-element models. Other major experimental steps forward included measuring spatial sound-pressure distributions, 3-D displacement patterns, and intracochlear pressures. Use of the finite-element method made it desirable to obtain detailed 3-D shape measurements, which were made much easier by the introduction of magnetic-resonance microscopy and x-ray microCT. The finite-element method has also made it possible to exploit measurements of material properties, and several different approaches have been used recently for making such measurements. The greatest challenges may be in dealing with very small dimensions and non-linear visco-elastic behavior. There is a need for more and better 3-D multipoint vibration measurements, and for material-property measurements that are more localized and that span a broader frequency range. Important directions for modeling include better use of available shape and material-property data, more attention to experimental animals and to variability, and better integration with cochlear models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".