A buckling region in locust hindlegs contains resilin and absorbs energy when jumping or kicking goes wrong
Bibliographic record
Abstract
If a hindleg of a locust slips during jumping, or misses its target during kicking, energy generated by the two extensor tibiae muscles is no longer expended in raising the body or striking a target. How, then, is the energy in a jump (4100-4800 μJ) or kick (1700 μJ) dissipated? A specialised buckling region found in the proximal hind-tibia where the bending moment is high, but not present in the other legs, buckled and allowed the distal part of the tibia to extend. In jumps when a hindleg slipped, it bent by a mean of 23±14 deg at a velocity of 13.4±9.5 deg ms(-1); in kicks that failed to contact a target it bent by 32±16 deg at a velocity of 32.9±9.5 deg ms(-1). It also buckled 8.5±4.0 deg at a rate of 0.063±0.005 deg ms(-1) when the tibia was prevented from flexing fully about the femur in preparation for both these movements. By experimentally buckling this region through 40 deg at velocities of 0.001-0.65 deg ms(-1), we showed that one hindleg could store about 870 μJ on bending, of which 210 μJ was dissipated back to the leg on release. A band of blue fluorescence was revealed at the buckling region under UV illumination that had the two key signatures of the elastic protein resilin. A group of campaniform sensilla 300 μm proximal to the buckling region responded to imposed buckling movements. The features of the buckling region show that it can act as a shock absorber as proposed previously when jumping and kicking movements go wrong.
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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) | 0.003 | 0.001 |
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".