Mating system of four inbreeding monkeyflower ( <i>Mimulus</i> ) species revealed using ‘progeny‐pair’ analysis of highly informative microsatellite markers
Bibliographic record
Abstract
Abstract The high variability of microsatellite markers has allowed more powerful and novel inferences regarding mating systems. We show that the many alleles at microsatellite loci allow a different approach for estimating a mating system that uses four‐gene coefficients of relationship; the four genes are those possessed by two progeny at a single locus. We estimated the mating system in four Mimulus taxa sampled from California, USA, all of which display inbreeding floral syndromes: Mimulus nasutus , Mimulus micranthus , Mimulus nudatus and Mimulus laciniatus . For each taxa, 20 progeny pairs were assayed for nine microsatellite loci. Substantial amounts of selfing were found, ranging from 64% to 92% across the taxa. Parent inbreeding coefficients were high (range 70%) in two taxa, and in these two taxa both facets of correlated matings (correlation of selfing and of outcrossed paternity) were also much higher (range 50–80%) than in the other two taxa. This association between correlated matings and levels of selfing has not been previously demonstrated. Microsatellites allow new approaches for mating system inference, but at the disadvantage of higher genotyping costs and potential biases resulting from null alleles.
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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 | 0.001 | 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".