Aspects in oat breeding: nutrition quality, nakedness and disease resistance, challenges and perspectives
Bibliographic record
Abstract
Abstract Traditionally, the oat crop (Avena sativa) has been neglected in a number of respects, cultivated in cropping areas not optimal for wheat, barley or maize. In recent years the interest in oats has increased, particularly because of its dietary benefits and therapeutic potential for human health. The uniqueness and advantages of naked oats over other popular cereals, due to its potentially valuable nutritional composition, have been well studied and reported, opening new market “niches” for oats. Despite the well‐documented benefits, the status of the oat crop is still fragile, due to many reasons. The area cultivated for the oat crop is much less compared with other cereals, and therefore commercial efforts in oat breeding are less. Oat groat yield is lower than other cereals such as wheat and the nutritious uniqueness has not been reflected in agreeable market prices. The same price still exists for both naked and conventional/covered oats in the world grain market. The absence of visible market competitiveness, and some of the oat biological drawbacks, including low grain yield, keeps the oat crop as a lower profitability minor crop. This review is intended to analyse and summarise main achievements and challenges in oat genetics, agronomy and phytopathology to find possible ways of oat improvement and future perspectives for oat breeding.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".