NATURAL SELECTION ON FLORAL TRAITS THROUGH MALE AND FEMALE FUNCTION IN WILD POPULATIONS OF THE HETEROSTYLOUS DAFFODIL<i>NARCISSUS TRIANDRUS</i>
Bibliographic record
Abstract
Measurements of natural selection in hermaphrodite populations require the analysis of performance through both female and male sex functions. Here, we investigate selection on three floral traits: flower number, flower length, and corona width through both sex functions in natural populations of the tristylous daffodil Narcissus triandrus. Selection through female function was examined in six populations, and in two of these we also estimated male selection gradients using multilocus microsatellite genotyping of parents and offspring. We detected significant directional selection for flower number through female function, and significant stabilizing selection for corona width and flower length through male function. Variation in male reproductive success was strongly influenced by the distance between mates and was significantly higher than variation in female reproductive success in one population, a result consistent with Bateman's principle. However, variation through both sex functions was similar in the other population and there was a significant negative correlation between female and male fitness indicating sex-specific trade-offs in reproductive success. Selection on floral design in N. triandrus was stronger through male than female function probably because floral morphology plays an important role in promoting effective cross-pollen transfer in populations of this heterostylous species.
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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 | 0.001 |
| 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".