Genetic variability of seed yield and oil nutritional attributes in linseed dominated by biennial variation
Bibliographic record
Abstract
Improving seed yield and oil quality of oilseed crops can aid in provision of a nutritious diet for humans. A worldwide collection of linseed was evaluated for seed yield (YLD), seed oil (OIL) and protein content (PRO), oil fatty acid composition, omega-3 to omega-6 ratio (ω3/ω6), total tocopherol content (TTC), and total phenolic content (TPC). At 2 years, higher temperature (~7%) and lower relative humidity (~16.6%) during the seed filling and maturity period (dryer condition) were correlated with significant decreases in YLD (~18%) and OIL (~4.5%), lower contents of linolenic acid (~13%) and TTC (9.8%), and lower ω3/ω6 ratio (~31%); oleic (~9%) and linoleic acid contents (~23%) and TPC (14.4%) increased. Correlation results demonstrated some significant associations among quantitative traits such as YLD, OIL, and thousand seed weight (TSW); however, the association of these traits with qualitative indices was mostly negative. Genotypes were classified irrespective of their geographical origin and independent of seed or flower colour. In this classification, a yellow-seeded Canadian group had the lowest ω3/ω6 ratio (~0.05), the highest seed yield and high TTC, whereas groups with the highest ω3/ω6 ratio (>3.0) had the lowest oil TTC and low to average seed yield. Results suggested that some brown-seeded Asian genotypes with high grain yield and oil potential, higher ω3/ω6 ratio, and other more stable oil quality indices are suitable to develop broadly adaptive varieties under the possible fluctuation of climatic factors. Other genetic groups could also be used for breeding programs with specific objectives.
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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 | 0.001 |
| 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".