Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (<i>Glycine max</i>(L.) Merrill) germplasm
Bibliographic record
Abstract
Linolenic acid and seed lipoxygenases are associated with off flavours in soybean products. F5 recombinant inbred lines (RILs) from a cross between a low linolenic acid line (RG10) and a seed lipoxygenase-free line (OX948) were genotyped for simple sequence repeats (SSR), random amplified polymorphic DNA (RAPD), sequence-tagged sites (STS), and cleaved amplified polymorphic sequence (CAPS) markers and evaluated for seed and agronomic traits at 3 Ontario locations in 2 years. One hundred twenty markers covering 1247.5 cM were mapped to 18 linkage groups (LGs) in the soybean composite genetic map. Seed lipoxygenases L-1 and L-2 mapped as single major genes to the same location on LG G13-F. L-3 mapped to LG G11-E. This is the first report of a map position for L-3. A major quantitative trait locus (QTL) associated with reduced linolenic acid content was identified on LG G3-B2. QTLs for 12 additional seed and agronomic traits were detected. Linolenic acid content, linoleic acid content, yield, seed mass, protein content, and plant height QTL were present in at least 4 of 6 environments. Three to 8 QTLs per trait were detected that accounted for up to 78% of total variation. Linolenic acid and lipoxygenase loci did not overlap yield QTL, suggesting that it should be possible to develop high-yielding lines resistant to oxidative degradation by marker-assisted selection (MAS).
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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".