Genetic Diversity in Natural Populations and Corresponding Seed Collections of Little Bluestem as Revealed by AFLP Markers
Bibliographic record
Abstract
Little bluestem [Schizachyrium scoparium (Michx.) Nash] is one of the most widespread native grasses in North America. Little is known about the genetic diversity of this species in natural populations and in seed collections. The amplified fragment length polymorphism (AFLP) technique was applied to assess the comparative genetic diversity of six natural populations of little bluestem in Manitoba and Saskatchewan and their corresponding seed collections. Five AFLP primer pairs were employed to screen a total of 180 samples representing about 15 tillers per population and 15 seeds per collection, and 158 polymorphic AFLP bands were scored for each sample. Analyses of these scored bands revealed that >91% of the total AFLP variation was present within the natural populations and within the seed collections. The among‐population and among‐collection variation components, although relatively small (7–9%), were statistically significant from zero. Comparisons of AFLP profiles between the seed and tiller samples revealed the seed samples had fewer polymorphic bands, higher average band frequencies, and more bands with extremely high or low frequencies. A significant association of AFLP variation with geographical origin was detected in the seed, but not the tiller samples. These results indicate collecting seeds may not be as effective as collecting tillers in sampling genetic diversity from natural populations for the improvement of little bluestem germplasm for rangeland seeding.
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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 | 0.001 |
| 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".