A new angle on clinging in geckos: incline, not substrate, triggers the deployment of the adhesive system
Bibliographic record
Abstract
Lizards commonly climb in complex three-dimensional habitats, and gekkotans are particularly adept at doing this by using an intricate adhesive system involving setae on the ventral surface of their digits. However, it is not clear whether geckos always deploy their adhesive system, given that doing so may result in decreased (i.e. reduction in speed) locomotor performance. Here, we investigate circumstances under which the adhesive apparatus of clinging geckos becomes operative, and examine the potential trade-offs between speed and clinging. We quantify locomotor kinematics of a gecko with adhesive capabilities (Tarentola mauritanica) and one without (Eublepharis macularius). Whereas, somewhat unusually, E. macularius did not suffer a decrease in locomotor performance with an increase in incline, T. mauritanica exhibited a significant decrease in speed between the level and a 10 degrees incline. We demonstrate that this results from the combined influence of slope and the deployment of the adhesive system. All individuals kept their digits hyperextended on the level, but three of the six individuals deployed their adhesive system on the 10 degrees incline, and they exhibited the greatest decrease in velocity. The deployment of the adhesive system was dependent on incline, not surface texture (600 grit sandpaper and Plexiglas), despite slippage occurring on the level Plexiglas substrate. Our results highlight the type of sensory feedback (gravity) necessary for deployment of the adhesive system, and the trade-offs associated with adhesion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".