Costs and economy of autotomy for tail movement and running speed in the skink <i>Trachylepis maculilabris</i>
Bibliographic record
Abstract
Economy of autotomy (shedding less than complete tails) is advantageous via retention of ability to autotomize and reduction of costs, including lipid loss, regeneration, and decreased social status. We studied its effects on predator-distracting tail movements and running speed in the speckle-lipped mabuya ( Trachylepis maculilabris (Boettger, 1913)) by removing fractions of the autotomizable portion. Distance moved was shorter for autotomized tail segments one third of the total tail length than for longer segments. Movement duration did not vary with proportion removed. Longer movement suggests that shedding longer segments improves ability to distract predators, enhances difficulty of capturing a tail, and may require longer handling time. Tails were difficult to break in regenerated sections and did not move when broken. The lack of movement of regenerated portions after separation suggests permanent loss of capacity to distract predators. Decreased speed was confirmed as a cost of autotomy in lizards that lost at least two thirds of their tail. In lizards that lost one third of tails speed was intermediate to that of intact lizards and those that lost more. Graded decrease in speed as proportional loss increases is consistent with progressive loss of a counterweight that reduces lateral motion while running. Economy of autotomy entails trade-offs between immediate and long-term escape ability.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".