Behavioural biomechanics: leaf-cutter ant cutting behaviour depends on leaf edge geometry
Bibliographic record
Abstract
Leaf-cutter ants cut fresh leaves to grow a symbiotic fungus as crop. During cutting, one mandible is typically anchored onto the leaf lamina while the other slices through it like a knife. When initiating cuts into the leaf edge, however, foragers sometimes deviate from this behaviour and instead use their mandibles symmetrically, akin to scissors. In vivo behavioural assays revealed that the preference for either of the two cutting strategies depended on leaf edge geometry and differed between natural leaf margins that were straight or serrated with notch-like folds: leaf-cutter ants displayed a strong preference for scissor-cutting when leaf edges were straight or had wide notches. This preference, however, reversed in favour of knife-cutting when notches were narrow. To investigate whether this behavioural difference had a mechanical origin, we mimicked knife-cutting in ex vivo cutting experiments: for wide notches, all but the sharpest mandibles failed to initiate cuts, or only did so at large forces, caused by substantial leaf buckling and bending. This increased force demand would substantially limit the ability of foragers to cut leaves, and so reduce the colony’s access to food sources. Scissor-cutting may thus be an adaptation to the mechanical difficulties associated with bending and buckling of thin leaves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".