Bibliographic record
Abstract
Some flowering plants signal the abundance of their rewards by changing their flower colour, scent or other floral traits as rewards are depleted. These floral trait changes can be regarded as honest signals of reward states for pollinators. Previous studies have hypothesized that these signals are used to maintain plant-level attractiveness to pollinators, but the evolutionary conditions leading to the development of honest signals have not been well investigated from a theoretical basis. We examined conditions leading to the evolution of honest reward signals in flowers by applying a theoretical model that included pollinator response and signal accuracy. We assumed that pollinators learn floral traits and plant locations in association with reward states and use this information to decide which flowers to visit. While manipulating the level of associative learning, we investigated optimal flower longevity, the proportion of reward and rewardless flowers, and honest- and dishonest-signalling strategies. We found that honest signals are evolutionarily stable only when flowers are visited by pollinators with both high and low learning abilities. These findings imply that behavioural variation in learning within a pollinator community can lead to the evolution of an honest signal even when there is no contribution of rewardless flowers to pollinator attractiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".