AFLP Variation in Four Blue Grama Seed Sources
Bibliographic record
Abstract
Blue grama [Bouteloua gracilis (Willd. ex Kunth) Lag. ex Griffiths] is one of the most widespread native grasses in western North America. Several blue grama seed sources are currently used for rangeland seeding, but little is known about the genetic diversity of these seed sources. Amplified fragment length polymorphism (AFLP) technique was applied to compare the genetic diversity among four blue grama seed sources (a precultivar germplasm [balanced multisite composite, BMSC], the ecotype Bad River, a Minnesota ecotype, and a native Manitoba seed collection) and to assess the genetic shift during two generations of BMSC seed multiplication. Germplasm BMSC was a balanced multisite composite of 99 clones selected from 495 live plants collected from 11 sites across Manitoba. Six AFLP primer pairs were employed to screen a total of 176 individual plants sampled from both the first three generations of BMSC and the other three seed sources and 167 polymorphic AFLP bands were scored for each plant. Large AFLP variation was observed within the four seed sources. Greater AFLP variation was detected in the BMSC than Bad River, Minnesota ecotype, and the Manitoba native harvest. No genetic shift in the BMSC was found across the two seed multiplications. These results indicate a balanced composite of multisite blue grama germplasm can maintain high genetic diversity with little genetic shift in a few generations of seed multiplication.
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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".