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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".