Density and distribution of cutaneous sensilla on tails of leopard geckos (<i>Eublepharis macularius</i>) in relation to caudal autotomy
Bibliographic record
Abstract
The lizard tail is well known for its ability to autotomize and regenerate. Physical contact of the tail by a predator may induce autotomy at the location at which the tail is grasped, and upon detachment the tail may undergo violent, rapid, and unpredictable movements that appear to be, to some degree, regulated by contact with the physical environment. Neither the mechanism by which tail breakage at a particular location is determined, nor that by which environmental feedback to the tail is received, are known. It has been suggested that mechanoreceptors (sensilla) are the means of mediation of such activities, and reports indicate that the density of sensilla on the tail is high. To determine the feasibility that mechanoreceptors are involved in such phenomena, we mapped scale form and the size, density, distribution, and spacing of sensilla on the head, body, limbs, and tail of the leopard gecko. This species has a full complement of autotomy planes along the length of the tail, and the postautotomic behavior of its tail has been documented. We found that the density of sensilla is highest on the tail relative to all other body regions examined; a dorsoventral gradient of caudal sensilla density is evident on the tail; sensilla are more closely spaced on the dorsal and lateral regions of the tail than elsewhere and are carried on relatively small scales; and that the whorls of scales on the tail bear a one to one relationship with the autotomy planes. Our results are consistent with the hypotheses of sensilla being involved in determining the site at which autotomy will occur, and with them being involved in the mediation of tail behavior following autotomy. These findings open the way for experimental neurological investigations of how autotomy is induced and how the detached tail responds to external environmental input.
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 | 0.000 | 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".