Identification of <i>Brassica</i> genotypes and molecular markers for increased seed oil content
Bibliographic record
Abstract
Javed, N., Tahir, M., Geng, J., Li, G. and McVetty, P. B. E. 2014. Identification of Brassica genotypes and molecular markers for increased seed oil content. Can. J. Plant Sci. 94: 1103–1108. Carbon dioxide emissions by the transportation sector are major contributors to global climate change. Lower CO2 emissions by the transportation sector are linked to the use of renewable fuels including biodiesel. Canola has high seed oil content, adaptation to temperate climates and favorable fatty acid composition, which make it a preferred feedstock for biodiesel production. Doubled haploid (DH) line, random inbred (RI) line and consensus genetics maps for mapping populations derived from Polo × Topas were developed. The DH line-based genetic map was then used for the identification and tagging of quantitative trait loci (QTL) controlling seed oil biosynthesis. This genetic map consisted of 620 loci identified using several different types of molecular markers, and covered a map distance of 2241.1 cM with marker saturation of 3.7 cM. The phenotypic data on the mapping population for seed oil content and component fatty acids were collected from four-environment replicated field trials. One hundred and thirty-one QTL for various fatty acids in canola oil and 14 QTL for oil content were identified. These QTL, combined with marker-assisted selection, may assist breeders in their attempts to develop canola lines with improved oil quality, oil content and oil production per hectare for biodiesel production.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".