Genetic variation for pseudo‐self‐compatibility in self‐incompatible populations of <i>Leavenworthia alabamica</i> (Brassicaceae)
Bibliographic record
Abstract
Self-incompatibility (SI) promotes outcrossing, but transitions to self-compatibility (SC) are frequent. Population genetic theory describing the breakdown of SI to SC suggests that, under most conditions, populations should be composed of either SI or SC individuals. Under a narrow range of conditions, theory suggests that SI may persist alongside reduced expression of SI (pseudo-SI, PSI) in mixed-mating populations. We studied genetic variation for PSI segregating in four SI populations of Leavenworthia alabamica by measurement of the heritability of pollen tube number after self-pollination. We tested for the role of the S-locus in this variation by sequencing seven S-alleles from plants with high pseudo-SC (PSC) and testing for the co-segregation of these alleles with PSC. We found a continuous distribution of PSC in all populations and 90% of plants exhibited PSC. The heritability ranged from 0.39 to 0.57. All seven S-alleles from plants with high PSC exhibited trans-specific polymorphism, and no stop codons were observed within the c. 600-bp region sequenced. One of these S-alleles was directly associated with the inheritance of PSC. We conclude that heritable variation in PSC is largely a result of genetic variation in the signaling cascade downstream of the S-locus reaction, together with the presence of one leaky S-allele.
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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) | 0.001 | 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".