Efferent innervation to the auditory basilar papilla of scincid lizards
Bibliographic record
Abstract
Hair cells of the inner ear of vertebrates are innervated by afferent neurons that transmit sensory information to the brain as well as efferent neurons that receive feedback from the brainstem. The function of the efferent feedback system is poorly understood and may have changed during evolution when different tetrapod groups acquired sensitivity to airborne sound and extended their hearing ranges to higher frequencies. Lizards show a unique subdivision of their basilar papilla (homologous to the mammalian organ of Corti) into a low-frequency (<1 kHz) and a high-frequency (approximately 1-5 kHz) region. The high-frequency region was reported to have lost its efferent innervation, suggesting it was insignificant or even functionally detrimental at higher frequencies. We re-examined the innervation to the basilar papilla of five species of Australian scincid lizards, by using immunohistochemistry. Anti-choline acetyltransferase (ChAT) was used as an efferent marker. Co-localization with anti-synaptic vesicle protein 2 confirmed the synaptic identity of label. Cholinergic terminals were observed along the whole length of the basilar papilla, including the regions that had previously been described as devoid of efferent innervation. However, there was a clear decrease in terminal density from apical, low-frequency to basal, high-frequency locations. Our findings suggest that efferent innervation is a general feature of the hair cells in the basilar papilla of lizards, irrespective of tonotopic location. This re-enforces the notion that efferent feedback control of hair cells is a fundamental and important property of all vertebrate hearing organs.
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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 | 0.000 |
| 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) | 0.001 | 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".