Bibliographic record
Abstract
Canola is an edible type of rapeseed that was developed in the 1970’s [17]. Rapeseed grown prior to that time had moderate levels of erucic acid, which was recognized to be harmful in laboratory rat tests (cholesterol elevation and reduced weight). Breeders in Canada developed rapeseed varieties with low erucic acid content [25]. In 1978, Varieties with less than 2 percent erucic acid were trademarked as “Canola. ” Canola varieties must also have less than 30 micromoles of glucosinolates per gram of oil-free meal. Glucosinolates will negatively affect the consumption of canola meal by animals, thus reducing its feeding value. Canola’s amino acid distribution is very complementary to soybean meal and the two meals are often included in the same feed ration. Feeding studies have shown that animals perform better when fed a mixture of the two meals than when fed either alone [17]. Canola oil has been recognized as the healthiest oil available to consumers with low saturated and high monounsaturated fatty acids and a unique level of the omega3 fatty acid, alpha-linolenic acid. Canola oil is usually blended with other vegetable oils for the production of various solid and liquid cooking oils and salad dressings. Canola proteins are currently not being sold into the human food market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.259 | 0.202 |
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".