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 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".