Performance of white oat cultivars for grain chemical content
Bibliographic record
Abstract
The chemical analysis of market-available white oat cultivars allows for their optimized use for processing, consumption, and in blocks of crosses. The purpose of this study was to evaluate the performance of white oat cultivars with regard to grain chemical content, to estimate the correlation between these traits, and to select potential REMAP markers for marker-assisted selection. Fifteen cultivars, grown in three environments, were evaluated for the contents of proteins; lipids; total, insoluble, and soluble dietary fiber; β-glucan; and carbohydrates. Eight cultivars were analysed with REMAP markers. The cultivars Brisasul and UPFA Gaudéria presented higher contents of β-glucan, while cultivars URS 21 and URS 22 had higher protein contents. For animal feed, the cultivars Barbarasul and FAPA Louise stood out with high lipid contents. Positive correlations between the contents of lipid and total dietary fiber, protein and insoluble dietary fiber, and total dietary fiber and insoluble dietary fiber were constant in different environments. Also, carbohydrate content was negatively correlated with protein, total dietary fiber, insoluble dietary fiber, and lipid content. A total of thirteen REMAP combinations were selected for variability studies regarding lipid and β-glucan contents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".