Oat Germination Characteristics Differ among Genotypes, Seed Sizes, and Osmotic Potentials
Bibliographic record
Abstract
Oat (Avena sativa L.) yield and quality on the northern Great Plains are consistently reduced by frequent drought and wild oat (Avena fatua L.) competition. Wild oat cannot be selectively removed from oat with herbicides. Identifying genotypes or seed size(s) with high germination potential under moisture stress may facilitate improved seedling vigor, stand establishment, and crop competitiveness. Therefore, a germination study was conducted to determine the effects of genotype and seed size on the germination of oat seed subjected to moisture stress. Large, medium, and small seeds of six common western Canadian oat genotypes were germinated in polyethylene glycol (PEG 8000) solutions with initial osmotic potentials ranging from 0 to −0.4 MPa at 5°C. Generalized linear mixed models fit to the data provided a statistically valid, appropriate, and convenient method to analyze germination data. In all genotypes examined, decreasing seed size and osmotic potential increased median germination time (MGT) and lowered final germination percentage (FGP). Among genotypes, CDC Bell had the fastest MGT while AC Mustang had the highest FGP. Delays in MGT and reductions in FGP resulting from increased moisture stress were similar to those observed in other cereals, suggesting that oat may be as capable of germinating under low spring soil moisture conditions as wheat and barley. The results of this study also indicate that large seed of genotypes such as AC Mustang and CDC Bell appear better suited to germinate under the range of osmotic potentials in this study. Although differences in MGT and FGP between seed sizes in this study were statistically significant, they were generally small and thus, oat germination characteristics may not be substantially improved by screening out small seed from farm‐saved seed.
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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 | 0.000 | 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) | 0.001 | 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".