Evaluating genetic variation and relationships among Puccinellia nuttalliana populations using amplified fragment length polymorphism markers
Bibliographic record
Abstract
Liu, Y., Fu, Y.-B. and Coulman, B. E. 2013. Evaluating genetic variation and relationships amongPuccinellia nuttallianapopulations using amplified fragment length polymorphism markers. Can. J. Plant Sci. 93: 1097-1104. Nuttall's salt-meadow, or alkali grass [Puccinellia nuttalliana (Shultes) Hitchc.], is a native grass species in North America, well known for its salt tolerance. Little information is available about the genetic diversity of natural populations of this species. Amplified fragment length polymorphism (AFLP) markers were used to examine the inter-population relationships and to compare variances within and among 23 populations collected from the Canadian Great Plains. Five AFLP primer pairs were employed to screen 15 genotypes (five sets of three half-sib plants) from each population, and 185 polymorphic AFLP bands were scored for each sample. The frequencies of these scored bands ranged from 0.02 to 0.99 with a mean of 0.60. The analysis of molecular variance revealed more than 96% of the total AFLP variation resided within populations. Populations were not highly differentiated with only 4% of the total AFLP variation residing among populations. A Mantel test revealed a significant but low correlation between genetic and geographic distances (r=0.29, P=0.024). Implications for P. nuttalliana conservation, germplasm sampling, and cultivar development are discussed.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".