Inbreeding depression is difficult to purge in self‐incompatible populations of <i>Leavenworthia alabamica</i>
Bibliographic record
Abstract
The extent to which inbreeding depression can be purged is a major determinant of mating system evolution and is important to conservation and crop improvement. Studies of inbreeding depression purging have not been conducted in self-incompatible plants before. An experimental ('ancestral') treatment was first created from self-incompatible plants of Leavenworthia alabamica. Lines derived from this population were maintained by self-pollination for three generations in the attempt to create a 'purged' population with fewer recessive, deleterious mutations of large effect. Fitness components and the frequency of malformed phenotypes were monitored in progeny derived from selfing and outcrossing in the ancestral and purged treatments. Fitness component means and inbreeding depression were largely unchanged by three generations of forced self-pollination, and there was no reduction in the frequency of plants exhibiting malformed phenotypes. Our findings indicate that inbreeding depression in this species is largely a result of mutations of mild effect, consistent with the observation that self-incompatibility is maintained in most populations of L. alabamica, despite the presence of genetic variants with weaker self-incompatibility. Moreover, although population theory suggests that deleterious mutations of large effect should be sheltered from selection in the region of self-incompatibility locus, our results do not support this prediction.
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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 | 0.000 | 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) | 0.001 | 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".