Genetic differentiation in an artificial population of the threatened plant<i>Lupinus oreganus</i>(Fabaceae)
Bibliographic record
Abstract
Reintroduction, supplemental planting for genetic rescue, and the creation of artificial seed production populations are common methods to conserve rare plant species, but empirical studies assessing the effects of artificial selection on genetic diversity are rare. I conducted a retrospective DNA genotyping study on an artificial population (hereinafter Office) of the threatened plant, Lupinus oreganus Heller, to determine whether the process of establishing the Office population facilitated genetic differentiation and if genetic diversity was maintained in the Office cohort. Genotyping indicated that uncommon maternal lineages (cpDNA haplotypes) were selected for in the artificial population and that the Office population was genetically distinct from both seed source patches. Furthermore, despite a small population size of seven individuals, cpDNA haplotype and nDNA simple sequence repeat allelic diversity was maintained in the surviving Office cohort. This study suggests that small artificial rare plant populations may be beneficial for capitalizing on the existing within-population genetic diversity, but they may also select for uncommon allelic diversity and facilitate genetic differentiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".