Visit larger displays but probe proportionally fewer flowers: counterintuitive behaviour of nectar‐collecting bumble bees achieves an ideal free distribution
Bibliographic record
Abstract
Summary 1. Patterns of pollinator responses to variation in floral display size have significance for pollen flow among plants. Here we test a theoretical model for explaining such patterns by simultaneously assessing bumble bee behaviour and nectar availability in two native stands of Cirsium purpuratum with different spatial densities. 2. A bumble bee (Bombus diversus) foraging on a plant remembered and avoided only one or two flower heads that it had probed before, so that the flower‐head revisitation rate increased as it stayed longer on a plant. Moreover, the revisitation rate increased less rapidly on larger displays. 3. The number of heads probed per plant increased less than proportionally with display size, and this increase was smaller at higher plant density. This pattern is consistent with our expectation that a bee leaves a plant when the cost of flower‐head revisitation exceeds that of interplant movement. However, bees left plants slightly earlier than predicted. 4. As predicted, the visitation rate of bees per plant showed a decelerated increase with floral display size, and this increase was greater at higher plant density. 5. As a result of these complementary changes in the number of heads probed per plant and visitation rate per plant across plant densities, nectar rewards per head were equalized among displays (an ideal free distribution was achieved).
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".