Pollinator visitation, pollen limitation, and selection on flower size through female function in contrasting habitats within a population of <i>Campanula persicifolia</i>
Bibliographic record
Abstract
We looked at whether flower visitation rates, pollen limitation on seed production, and phenotypic selection on flower size through female function varied across a sharp gradient in light intensity (open meadow vs. forest) within a population of the perennial plant Campanula persicifolia L. (Campanulaceae). Flower visitation rates of putative pollinators were similar in both habitats. Seed number per fruit was strongly pollen limited, with no difference in the magnitude of pollen limitation between the two habitats. This strong pollen limitation set the basis for significant phenotypic selection, through female function, on a trait that probably is important for pollinator attraction: flower size. This was revealed by path analysis and structural equation modelling. The lack of difference in pollen limitation on seed production in the two habitats may be explained by the similarity in flower visitation rates in the two habitats. Moreover, the similarity in pollen limitation in the two habitats probably resulted in a similar magnitude and direction of selection on flower size through female function. Our results suggest that pollen limitation and selection through female function may vary little across space within a popualtion despite large variation in the environmental conditions experienced by plants.
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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 | 0.000 |
| 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".