Characterizing genetically diverse blue grama [<i>Bouteloua gracilis</i> (Willd. ex Kunth) Lag. ex Griffiths] seed sources
Bibliographic record
Abstract
The need for regionally adapted native grass seed sources for the northern Great Plains has resulted in the commercial release of a range of plant materials, including ecovars™ 1 . Ecovars™ are multisite composite populations developed to combine broad genetic diversity from a geographic region. The objective of this study was to determine whether morphological data could be used to distinguish between genetically diverse blue grama [Bouteloua gracilis (Willd. ex Kunth) Lag. ex Griffiths] seed sources through classical statistical methods. Entries included a Manitoba (MB) ecovar™, a USDA–NRCS released cultivar ‘Bad River’, and ecotypes from Wyoming and Minnesota. Vegetative and reproductive measurements and ratings were taken from a spaced-plant nursery during 2000–2001 in Carman, Manitoba, Canada. The results were analyzed using statistical techniques including: ANOVA, least significant difference, canonical discriminant analysis (CDA), and coefficients of variation. These techniques distinguished four genetically diverse seed sources from each other through CDA. As hypothesized, there was greater within-population genetic diversity for the MB ecovar™ and Wyoming and Minnesota ecotypes, compared with ‘Bad River’. Our results indicate that genetically diverse blue grama seed sources can be distinguished, based on phenotypic measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".