Analysis of the Acoustic Transcranial Bone Conduction
Bibliographic record
Abstract
OBJECTIVES: (1) To analyze the preferential pathways of sound transmission and sound waves travelling properties in the skull and (2) to identify the location(s) on the skull where bone conduction to the cochlea is optimal. STUDY DESIGN: Basic research Methods: Nine cadaveric heads were placed in an anechoic chamber and equipped with six Bone Anchored Hearing Aids (BAHA™) implants (Cochlear™, Sydney, NSW, Australia) and fifteen accelerometers. A laser velocimeter was used to measure cochlear response by placing a reflector on the round window. Different frequency sweeps were applied to each implant, and measurements were recorded simultaneously by the laser velocimeter and accelerometers. RESULTS: Low-frequency sound waves mostly travel the frontal transmission pathways, and there is no clear predominant pattern for the high frequencies. The mean inter-aural time lag is 0.1 ms. Optimal sound transmission to the cochlea occurs between 1000 and 2500 Hz with a contralateral 5 to 10 dB attenuation. The implant location does not influence mean transmission to the cochlea. CONCLUSION: There is a pattern of transmission for low frequencies through a frontal pathway but none for high frequencies. We were also able to demonstrate that the localization of the BAHA™ implant on the skull had no significant impact on the sound transmission, either ipsi or contralaterally.
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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) | 0.003 | 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".