Bumblebees avoid flowers containing evidence of past predation events
Bibliographic record
Abstract
Bees are at risk of predation from flower-dwelling ambush predators. Since these predators tend to be cryptic, bees trying to mitigate predation risk may need to make use of indirect cues of the predator’s presence. For example, they may use cues of past predation events as indirect cues of current predation risk. I conducted a series of experiments that examined how bumblebees (genus Bombus Latreille, 1802) respond to cues of past predation events. In two field experiments, I found that wild rose (genus Rosa L.) flowers containing a freshly killed bumblebee, or the scent of a freshly killed bumblebee, received fewer bumblebee visits than control flowers. To test the possibility that bumblebees in the first two experiments were avoiding cues of competition risk rather than cues of predation risk, captive-reared bumblebees were given a choice between two artificial flowers — one containing a freeze-killed bumblebee (relatively more similar to a live foraging bee) and the other containing a crush-killed bumblebee (relatively more similar to a bee killed by a predator). Most bumblebees chose the flower containing the freeze-killed bee, supporting the hypothesis that the bumblebees in the first two experiments were attempting to avoid predation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".