Prey capture in the Venus flytrap: collection or selection?
Bibliographic record
Abstract
Charles Darwin first proposed that the Venus flytrap ( Dionaea muscipula Ellis) functions optimally by capturing and digesting large prey, the small prey escaping through openings at the trap margins. This hypothesis, although intuitively sound in the context of trap mechanics or plant allocation theory, has not been tested adequately with populations of plants growing in the field. Here, with traps collected in the endemic habitat over 9 months, we show that prey capture in the Venus flytrap is opportunistic rather than selective. While there was no effect of trap size on prey capture success, there was a significant but weak positive relationship between trap length and prey length. Prey sizes were well below the theoretical maximum holding capacities of traps and relatively small insects were represented across the range of trap sizes. Our results show that prey capture was not biased toward large invertebrates. Instead, we suggest that nonselective prey capture across the observed range of trap sizes is the best-fit explanation of trap function in the context of relatively limited ability to change allocation in response to sudden increases in resource availability.
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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) | 0.000 | 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".