<i>Brassica carinata</i> – a new molecular farming platform for delivering bio‐industrial oil feedstocks: case studies of genetic modifications to improve very long‐chain fatty acid and oil content in seeds
Bibliographic record
Abstract
Abstract Crop development and species diversity are important aspects of the emerging global bioeconomy, as is maximizing crop value through total crop utilization. We advocate development of Brassica carinata as a biorefinery and bioindustrial oils platform using traditional and molecular breeding techniques and tools. We review genetic studies and breeding efforts to develop elite B. carinata germplasm, work involving development of transformation and regeneration protocols, target gene isolation, and transgene expression. Genetic modification strategies using a B. carinata breeding line as a delivery platform for very long‐chain fatty acid‐enhanced/modified oils are presented as case studies. The target oil products are erucic acid (22:1 Δ13), docosadienoic acid (22:2 Δ5, Δ13) and nervonic acid (24:1 Δ15); in addition transgenic efforts to enhance B. carinata seed oil content are discussed. The overall advantages and current limitations to utilizing this crop are delineated. Other anticipated biobased products from a B. carinata platform may include, but are not limited to, the production of biolubricants, biofuels and biopolymers from the oil, biopesticides, antioxidants, as well as plant gums, and vegetable protein‐based bioplastics and novel food and feed products. In summation, this collaborative B. carinata breeding/germplasm development/value‐added molecular modification effort will not only contribute to the development of renewable feedstocks for the emerging Canadian bioeconomy (biorefinery/bioproducts), but also promises to generate positive economic and environmental benefits. Published in 2010 by John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".