Inter‐ and intraspecific differences in leaf beetle attachment on rigid and compliant substrates
Bibliographic record
Abstract
Abstract The influence of substrate stiffness on the attachment ability of insects has been largely neglected so far. In the present study, traction experiments with adult beetles Gastrophysa viridula and Leptinotarsa decemlineata were carried out to study the influence of smooth, non‐structured surfaces, having different stiffness, on beetle attachment. Force measurements were performed with tethered walking adult insects, both males and females, intact and after removal of claws, on hydrophilic (normal) and hydrophobic (silanized) glass and four polydimethylsiloxane ( PDMS ) substrates ranging from 0.3 to 20 MP a in elastic modulus. Adult G. viridula generated higher safety factors (force/body weight) than L. decemlineata , 4.6–54.0 and 0.5–15.8 (min.–max.) respectively. The results show that the amputation of claws had no significant influence on the force generation by beetles on these smooth substrates. Males and females of both species performed best on stiffer surfaces having elastic moduli larger than 5 MP a. On the softer substrates, forces and safety factors significantly decreased. This effect was more prominent in L. decemlineata compared to the ten times lighter G. viridula . Since some natural substrates are rather soft, it is assumed that the effect of decrease in attachment ability of leaf beetles on soft substrata is of potential importance for the biology of leaf beetles.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".