Mating strategies in flowering plants: the outcrossing–selfing paradigm and beyond
Bibliographic record
Abstract
Comparisons of the causes and consequences of cross- and self-fertilization have dominated research on plant mating since Darwin's seminal work on plant reproduction. Here, I provide examples of these accomplishments, but also illustrate new approaches that emphasize the role of floral design and display in pollen dispersal and fitness gain through male function. Wide variation in outcrossing rate characterizes animal-pollinated plants. In species with large floral displays, part of the selfing component of mixed mating can arise from geitonogamy and be maladaptive because of strong inbreeding depression and pollen discounting. Floral strategies that separate the benefits of floral display from the mating costs associated with geitonogamy can resolve these conflicts by reducing lost mating opportunities through male function. The results from experiments with marker genes and floral manipulations provide evidence for the function of herkogamy and dichogamy in reducing self-pollination and promoting pollen dispersal. Evidence is also presented indicating that increased selfing resulting from changes to floral design, or geitonogamy in large clones, can act as a stimulus for the evolution of dioecy. The scope of future research on mating strategies needs to be broadened to include investigations of functional links among flowers, inflorescences and plant architecture within the framework of life-history evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".