Genetic variability of seed yield and oil nutritional attributes in linseed dominated by biennial variation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it