Going Out on a Limb: How Investigation of the Anoline Adhesive System Can Enhance Our Understanding of Fibrillar Adhesion
Bibliographic record
Abstract
The remarkable ability of geckos to adhere to a wide-variety of surfaces has served as an inspiration for hundreds of studies spanning the disciplines of biomechanics, functional morphology, ecology, evolution, materials science, chemistry, and physics. The multifunctional properties (e.g., self-cleaning, controlled releasability, reversibility) and adhesive performance of the gekkotan adhesive system have motivated researchers to design and fabricate gecko-inspired synthetic adhesives of various materials and properties. However, many challenges remain in our attempts to replicate the properties and performance of this complex, hierarchical fibrillar adhesive system, stemming from fundamental, but unanswered, questions about how fibrillar adhesion operates. Such questions involve the role of fibril morphology in adhesive performance and how the gekkotan adhesive apparatus is utilized in nature. Similar fibrillar adhesive systems have, however, evolved independently in two other lineages of lizards (anoles and skinks) and potentially provide alternate avenues for addressing these fundamental questions. Anoles are the most promising group because they have been the subject of intensive ecological and evolutionary study for several decades, are highly speciose, and indeed are advocated as squamate model organisms. Surprisingly, however, comparatively little is known about the morphology, performance, and properties of their convergently-evolved adhesive arrays. Although many researchers consider the performance of the adhesive system of Anolis lizards to be less accomplished than its gekkotan counterpart, we argue here that Anolis lizards are prime candidates for exploring the fundamentals of fibrillar adhesion. Studying the less complex morphology of the anoline adhesive system has the potential to enhance our understanding of fibril morphology and its relationship to the multifunctional performance of fibrillar adhesive systems. Furthermore, the abundance of existing data on the ecology and evolution of anoles provides an excellent framework for testing hypotheses about the influence of habitat microstructure on the performance, behavior, and evolution of lizards with subdigital adhesive pads.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".