Bee, hummingbird, or mixed-pollinated <i>Salvia</i> species mirror pathways to pollination optimization: a morphometric analysis based on the Pareto front concept
Bibliographic record
Abstract
Optimization of flower phenotypes to ensure pollination by agents differing in their match with fertile flower structures can involve fitness trade-offs if the aspects of the phenotype that enhance the fitness contribution of one pollinator are detrimental for pollination by the other agents. If these trade-offs are substantial, flower optimization for specialized pollination is expected. However, optimization for generalized pollination may also take place in trade-off scenarios, as long as the joint contribution of two or more types of pollinators to global pollination fitness is greater than each individual contribution. We used an observational approach to evaluate the role of pollination fitness trade-offs in flower trait optimization, a matter seldom addressed because of the difficulties in conducting experiments. A pattern-searching tool based on the Pareto front concept, borrowed from the fields of economics and engineering, was used to test for fitness trade-off patterns in the flower shape of four Salvia (Lamiaceae) species. Two are pollinated exclusively either by bees or by hummingbirds; the remaining species have mixed-pollination systems, with varying contributions of bee and hummingbird pollination. The patterning of flower shape in this study suggests a bee–hummingbird pollination trade-off in Salvia, and the optimization of generalized flower shapes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".