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: $$\triangleright $$ ▹ the ears of mammals are encased in the hardest bone in the body; $$\triangleright $$ ▹ the ear contains the most vascularized tissue in body; $$\triangleright $$ ▹ the ear has the highest resting potential in the body; $$\triangleright $$ ▹ ears have a unique “fingerprint”; $$\triangleright $$ ▹ the ear can detect signals below the thermal noise floor; and $$\triangleright $$ ▹ 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".