The Interaction of the Soybean Seed High Oleic Acid Oil Trait With Other Fatty Acid Modifications
Bibliographic record
Abstract
Abstract Oil value is determined by the functional qualities imparted from the fatty acid profile. Soybean oil historically had excellent use in foods and industry; the need to increase the stability of the oil without negative health consequences has led to a decline in soybean oil use. One solution to make the oil stable is to have high oleic acid (>70%) and lower linolenic acid content in the oil. Other fatty acid profile changes are intended to target market needs: low‐saturated fatty acid and high stearic acid content in the oil. The objective of this study is to determine the interaction of the high oleic acid oil trait with other alleles controlling fatty acid profiles. Soybean lines containing high oleic acid allele combinations plus other fatty acid modifying alleles were produced, and the seed was produced in multiple field environments over 2 years. Stable high oleic acid with low linolenic acid (<3.0%) was achieved with a 4‐allele combination. The target of >20% stearic acid in the seed oil was not achieved. Reducing total saturated fatty acids below 7% in a high oleic acid background was possible with mutant alleles of both an acyl‐ACP thioesterase B and a β‐ketoacyl‐[acyl‐carrier‐protein] synthase III gene. The results identified allele combinations that met the target fatty acid profile thresholds and were most stable across environments.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".