Genetic diversity of side-oats grama [<i>Bouteloua curtipendula</i>(Michx.) Torr.] populations as revealed by amplified fragment length polymorphism markers
Bibliographic record
Abstract
Biligetu, B., Schellenberg, M. P. and Fu, Y.B. 2013. Genetic diversity of side-oats grama [Bouteloua curtipendula (Michx.) Torr.] populations as revealed by amplified fragment length polymorphism markers. Can. J. Plant Sci. 93: 1105–1114. Side-oats grama [Bouteloua curtipendula (Michx.) Torr.] is a warm-season grass widely distributed in North America and is considered as an important grass for reclamation and summer forage production in drier regions. This study assessed genetic diversity of nine wild populations of side-oats grama grass, their corresponding balanced multiple-site composite (BMSC), and a population selected for forage type (FT), using the amplified fragment length polymorphism (AFLP) technique. Five AFLP primer pairs were employed to genotype 157 plants, and 312 polymorphic AFLP bands were detected. The frequencies of AFLP bands ranged from 0.01 to 0.99, and averaged 0.39. The AFLP analysis revealed 6% of the total AFLP variation present among the nine wild populations and 94% of variation within populations. The Minto population had the largest within-population diversity, while the FT population the lowest based on AFLP band richness and polymorphic loci. The BMSC population displayed significant genetic differentiation from the wild populations, but still captured substantial genetic diversity. Bayesian cluster analysis using BAPS and STRUCTURE programs revealed three and four optimal clusters of populations that explained 9.8 and 9.5% of the total AFLP variation, respectively. A genetic clustering of individual plants displayed no clear genetic separations among wild, FT, and BMSC populations, but the FT population showed some level of genetic shift, indicating the initial impact of artificial selection. These findings are significant for our understanding of the genetic diversity of side-oats grama. Large genetic variation present within populations provides a potential for further genetic enhancement.
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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) | 0.000 | 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".