Real-world challenges to, and capabilities of, the gekkotan adhesive system: contrasting the rough and the smooth
Bibliographic record
Abstract
Many species of gekkotan lizards possess adhesive subdigital pads that allow them to adhere to, and move easily on, a wide variety of surfaces. However, although the mechanism of adhesion and the potential adhesive capacity of this system have been extensively studied, the adaptive value of these structures and their deployment in natural situations have rarely been examined. The maximal adhesive capacity of gekkotan setal fields has been shown to greatly exceed the force needed to support the body. This high adhesive potential is likely an adaptation for movement on the natural surfaces that these lizards encounter in their environment. Natural surfaces may be rough, undulant, and unpredictable, and provide only limited, patchy areas with which adhesive structures can make contact. Here we examine the microtopography of rock surfaces used by a southern African species of gecko of the genus Rhoptropus Peters, 1869, and compare this to the form, configuration, compliance, and functional morphology of the setal fields of this species. Our results demonstrate that the structure and topology of natural surfaces are important factors in understanding the design of subdigital pads, and provide insight into the evolution of the adhesive system of gekkonid lizards and its adaptive value on topographically unpredictable surfaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".