Jumping from the surface of water by the long-legged fly<i>Hydrophorus</i>(Diptera, Dolichopodidae)
Bibliographic record
Abstract
The fly Hydrophorus alboflorens (4 mm long, 4.7 mg mass) moves around upon and jumps from water without its tarsi penetrating the surface. All six tarsi have a surface area of 1.3 mm(-2) in contact with the water, but they did not dimple its surface when standing. Jumping was propelled by depression of the trochantera of both hind and middle legs, which are 40% longer than the front legs and 170% longer than the body. As these four legs progressively propelled the insect to take-off, they each created dimples on the water surface that expanded in depth and area. No dimples were associated with the front legs, which were not moved in a consistent sequence. The wings opened while the legs were moving and then flapped at a frequency of 148 Hz. The body was accelerated in a mean time of 21 ms to a mean take-off velocity of 0.7 m s(-1). The best jumps reached velocities of 1.6 m s(-1), and required an energy output of 7 μJ and a power output of 0.6 mW, with the fly experiencing a force of 140 g. The required power output indicates that direct muscle contractions could propel the jump without the need for elaborate mechanisms for energy storage. Take-off trajectories were steep, with a mean of 87 deg to the horizontal. Take-off velocity fell if a propulsive tarsus penetrated the surface of the water. If more tarsi became submerged, take-off was not successful. A second strategy for take-off was powered only by the wings and was associated with slower (1 deg ms(-1) compared with 10 deg ms(-1) when jumping) and less extensive movements of the propulsive joints of the middle and hind legs. No dimples were then created on the surface of the water. When jumping was combined with wing flapping, the acceleration time to take-off was reduced by 84% and the take-off velocity was increased by 168%. Jumping can potentially therefore enhance survival when threatened by a potential predator.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".