Camelina breeding and development- a Canadian perspective
Bibliographic record
Abstract
Climate change is predicted to have a more profound impact on the Canadian Prairies compared to other regions in the world, with higher relative temperatures, longer periods of water stress and increased frequency of droughts. Camelina sativa (camelina) is a promising alternative, climate-resilient oilseed that could become part of a Canadian strategy to battle climate change and its detrimental effects on agriculture. Albeit currently a small crop, camelina has enormous potential for growth: favorable agronomics, like early maturity, frost and drought tolerance, pest and disease resistance, as well as exceptional winter hardiness in true winter types in combination with a unique oil profile render it an excellent feedstock crop not only for biofuel, but also high value feed and food uses. Uses for camelina oil and meal include industrial applications (e.g. biodiesel, lubricants, and polymers) and higher value areas such as cosmetics, Omega-3 supplements for human and companion animal nutrition, and applications in the livestock, poultry and aquaculture feed sectors. As a relatively undeveloped crop, there is significant potential for improvement of both agronomic and seed quality characteristics. This presentation will provide an overview of current camelina breeding and crop development efforts underway at the AAFC Research and Development Center in Saskatoon in collaboration with industry. This includes variety and germplasm development in spring- and winter-type camelina, insights into the genomics of camelina as well as recent developments in the Canadian camelina industry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".