Disassortative mating and the maintenance of sexual polymorphism in painted maple
Bibliographic record
Abstract
Since Darwin's pioneering research on plant reproductive biology (e.g. Darwin 1877), understanding the mechanisms maintaining the diverse sexual strategies of plants has remained an important challenge for evolutionary biologists. In some species, populations are sexually polymorphic and contain two or more mating morphs (sex phenotypes). Differences in morphology or phenology among the morphs influence patterns of non-random mating. In these populations, negative frequency-dependent selection arising from disassortative (intermorph) mating is usually required for the evolutionary maintenance of sexual polymorphism, but few studies have demonstrated the required patterns of non-random mating. In the current issue of Molecular Ecology, Shang et al. (2012) make an important contribution to our understanding of how disassortative mating influences sex phenotype ratios in Acer pictum subsp. mono (painted maple), a heterodichogamous, deciduous tree of eastern China. They monitored sex expression in 97 adults and used paternity analysis of open-pollinated seed to examine disassortative mating among three sex phenotypes. Using a deterministic 'pollen transfer' model, Shang et al. present convincing evidence that differences in the degree of disassortative mating in progeny arrays of the sex phenotypes can explain their uneven frequencies in the adult population. This study provides a useful example of how the deployment of genetic markers, demographic monitoring and modelling can be integrated to investigate the maintenance of sexual diversity in 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 | 0.000 |
| 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) | 0.000 | 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".