Rapeseed and canola oil: production, processing, properties and uses
Bibliographic record
Abstract
1. Rapeseeds and rapeseed oil -- agronomy, production, and trade. Elaine J. Booth, Scottish Agricultural College, Aberdeenshire, UK and Frank D. Gunstone, University of St Andrews and Scottish Crop Research Institute, Dundee, UK. 2. Extraction and refining. Elaine J. Booth, Scottish Agricultural College, Aberdeenshire, UK. 3. Chemical composition of canola and rapeseed oils. W. Nimal Ratnayake, Health Canada, Ottawa, Canada and James K. Daun, Grain Research Laboratory, Winnipeg, Canada. 4. Chemical and physical properties of canola and rapeseed oil. Derick Rousseau, Ryerson University , Toronto, Canada. 5. High erucic oil: its production and uses. Clare Temple--Heald, Croda Chemicals Europe Ltd, Hull, UK. 6. Food uses and nutritional properties. Bruce E. McDonald, Manitoba Health Research Council, Winnipeg, Canada. 7. Non--food uses. Kerr Walker, Scottish Agricultural College, Aberdeenshire, UK. 8. Potential and future prospects for rapeseed oil. Christian Mollers, University of Goettingen, Germany. References. Index
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.033 |
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".