Innate or learned preference for upward-facing flowers?: implications for the costs of pendent flowers from experiments on captive bumble bees
Bibliographic record
Abstract
Pollinator preferences for phenotypic characters, including floral orientation, can affect plant reproductive success. For example, hawkmoths and syrphid flies prefer upward- over downward-facing flowers in field experiments. Although such preferences suggest a cost of pendent flowers in terms of pollinator attraction, we cannot rule out the possibility that the preferences have been affected by prior experience: pollinators might choose the same type of flowers to which they have already become accustomed. To test for innate preference, we observed bumble bees foraging on an array of upward- and downward-facing artificial flowers. Without any prior experience with vertical flowers, 91.7% bees chose an upward-facing flower at the very first visit. In addition to this innate preference, we also found that the preference was strengthened by experience, which suggests that the bees learned upward-facing flowers were easier to handle. Although bumble bees may concentrate on pendent flowers in the field, such learned preferences are evidently imposed on a template of upward-facing preference. Because bee-pollinated pendent flowers face particular difficulties in attracting visits, therefore, we expect them to compensate through other means, such as greater floral rewards.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".