QUANTITATIVE GENETIC VARIATION IN POPULATIONS OF<i>AMSINCKIA SPECTABILIS</i>THAT DIFFER IN RATE OF SELF-FERTILIZATION
Bibliographic record
Abstract
Self-fertilization is expected to reduce genetic diversity within populations and consequently to limit adaptability to changing environments. Little is known, however, about the way the evolution of self-fertilization changes the amount or pattern of the components of genetic variation in natural populations. In this study, a reciprocal North Carolina II design and maximum-likelihood methods were implemented to investigate the genetic basis of variation for 15 floral and vegetative traits in four populations of the annual plant Amsinckia spectabilis (Boraginaceae) differing in mating system. Six variance components were estimated according to Cockerham and Weir's "bio" model c. Compared to the three partially selfing populations, we found significantly lower levels of nuclear variance for several traits in the nearly completely self-fertilizing population. Furthermore, for 11 of 15 traits we did not detect nuclear variation to be significantly greater than zero. We also found high maternal variance in one of the partially selfing populations for several traits, and little dominance variance in any population. These results are in agreement with the evolutionary dead-end hypothesis for highly self-fertilizing taxa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".