How Exceptional Is the Ear?
Bibliographic record
Abstract
Abstract Studies of hearing often conclude that the ear is “remarkable” or that its performance is “exceptional.” Some common examples include the following: $$\triangleright $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>▹</mml:mo> </mml:math> the ears of mammals are encased in the hardest bone in the body; $$\triangleright $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>▹</mml:mo> </mml:math> the ear contains the most vascularized tissue in body; $$\triangleright $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>▹</mml:mo> </mml:math> the ear has the highest resting potential in the body; $$\triangleright $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>▹</mml:mo> </mml:math> ears have a unique “fingerprint”; $$\triangleright $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>▹</mml:mo> </mml:math> the ear can detect signals below the thermal noise floor; and $$\triangleright $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>▹</mml:mo> </mml:math> the ear is highly nonlinear (or highly linear, depending upon who you ask). Some claims hold up to further scrutiny, while others do not. Additionally, several claims hold for animals in one taxon, while others are shared across taxa. Most frequently, our sense of wonder results from the differences between ears as products of natural selection (over eons) and artificial systems as products of engineering design. Our goal in analyzing claims of remarkable or exceptional performance is to deepen our appreciation of these differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".