Genotypic and environmental effects on saturated fatty acid concentration of canola grown in Manitoba
Bibliographic record
Abstract
The low saturated fatty acid concentration of canola oil relative to other vegetable oils has resulted in a favourable market share for canola. Understanding the effects of genotype and environment on saturated fatty acid concentration will facilitate Brassica napus breeding efforts aimed at maintaining or reducing saturate levels in the seed oil. Canola-quality B. napus samples from the Manitoba Crop Variety Evaluation Team (MCVET) trials in 1999, 2000 and 2001 were tested for fatty acid composition, oil concentration and protein concentration. Weather data were obtained from nearby weather stations. The majority of the variation in total saturates for the cultivars studied was attributed to variation in palmitic acid (C16:0) due to the genotype main effect and variation in stearic acid (C18:0) due to the genotype and environment main effects. The variation due to the genotype × environment interaction was small relative to the main effects for the individual saturated fatty acids. C18:0 and arachidic (C20:0) acid concentrations were correlated between genotypes, suggesting pleiotropy or that these traits are controlled by linked genes in the cultivars tested. The data also suggested that C16:0 and C18:0 acid concentrations are controlled by different genes. C18:0, C20:0 and behenic (C22:0) acid concentrations were correlated across environments, but did not correlate with C16:0 concentration. Relationships between weather variables and fatty acid composition, oil concentration and protein concentration were not established. Additive Main effects and Multiplicative Interaction (AMMI) analysis revealed that some canola cultivars were more stable than others with regard to total saturates over environments. Key words: Genotype, environment, seed quality, saturated fatty acids, canola, Brassica napus
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".