SNP‐RFLP Markers for the Study of <i>Arabidopsis lyrata</i>
Bibliographic record
Abstract
ABSTRACT Arabidopsis lyrata has become a useful system for the study of comparative genomics, hybridization, polyploidization, and evolutionary transitions from outcrossing to selfing. Previous studies of its mating system have used microsatellite loci, but low allelic diversity, particularly in self‐compatible populations characterized by low levels of outcrossing, reduces the utility of these markers for more detailed studies. Here, we aimed to develop population‐level SNP markers for A. lyrata ssp. lyrata sampled from a self‐compatible population at Rondeau Provincial Park, Ontario, Canada. We performed de novo SNP discovery and identified 6808 putative SNPs from genome‐wide sequences of 22 individuals originating from a highly selfing population. Further filtering and marker validation enabled the development of 17 SNP marker loci that can be visualized using standard PCR‐RFLP protocols. These markers had average minor‐allele frequencies of 0.40 in the target population, and four of seven markers were variable in a small sample from nine other A. lyrata populations. These PCR‐RFLP markers have the potential to be useful for the analysis of mating patterns within and beyond the inbred self‐compatible populations of A. lyrata studied here and enable the continued development of A. lyrata as a model for studying evolutionary transitions from outcrossing to selfing.
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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 | 0.001 |
| 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".