Empty flowers as a pollination-enhancement strategy
Bibliographic record
Abstract
Question: Can the reproductive benefits gained by mitigating the costs of self-pollination drive the evolution of nectarless flowers? Features of model: Complementary analytical and simulation models determined the optimal proportion of nectarless flowers (‘nectar phenotype’) to maximize male reproductive success. Models considered a range of self-pollination costs and pollinator abundances. In the analytical model, equal numbers of each nectar phenotype were present. Pollinators used simple rules of behaviour, related to their current host plant’s perceived nectar status, to decide whether to stay on that plant or to move to a new plant. In the simulation model, pollinators used more sophisticated departure rules, comparing the current host plant’s perceived nectar status to the population mean. Plants with different proportions of nectarless flowers competed for successful pollination over multiple seasons. Ranges of key variables: Relative cost of self-pollination (0.5–1); number of pollinators acting on a plant population per season (1–101); and proportion of nectarless flowers per plant (0–1). Conclusions: Enhanced pollination success can drive the evolution of empty flowers in plants that are reliant on vector-mediated pollination. When the costs of selfing are low, an inflorescence with a low proportion of nectarless flowers is optimal, because pollination success is primarily determined by pollen removal. When the costs of selfing are high, an inflorescence with mostly nectarless flowers is optimal, because pollination success is primarily determined by outcrossing. Low pollinator abundances lead to a decreased optimal proportion of empty flowers to mitigate pollinator limitation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".