Environmental effects on fatty acid levels in soybean seed oil
Bibliographic record
Abstract
Abstract FA composition determines the quality of vegetable oil. Soybean breeders have generated and used mutations in FA genes to develop altered FA profiles in the seed. However, the expression of the alleles and the relative activity of the gene products are often dependent on the environment, and these facts have hampered the breeding efforts. To investigate the environmental effect on FA composition of soybean seed oil in specific mutant material developed at the University of Guelph, a recombinant inbred line (RIL) population was developed from a cross between a low palmitate (16∶0) line and a high‐stearate (18∶0) parent. The RIL population was field‐tested across three environments over 2 yr. A combined ANOVA for FA composition was conducted to determine the year and location effects on the expression of FA alleles in this material. The results indicated that linolenic (18∶3) level was most vulnerable to the environmental changes. Year effects accounted for a greater amount of variance than location effects for 16∶0, 18∶0, and 18∶1, whereas location effects were more important than year effects for the relative amounts of 18∶2 and 18∶3. Genotype × environment (year, location) interaction effects were significant for the relative amounts of all five FA according to the combined ANOVA. Our results indicated that the extreme minimum daily temperatures during September seed fill period, rather than the means or the maximum temperature, may be responsible for the ratio of saturated vs. unsaturated FA in soybean oil.
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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".