Physical, chemical, and lubricant properties of Brassicaceae oil
Bibliographic record
Abstract
Rapeseed oil and canola oil have traditionally been used in industrial lubricant applications but oils of many species of Brassicaceae have similar properties. Oil from the seed of seven Brassicaceae species, Sinapis alba (yellow mustard), Camelina sativa (false flax), Brassica carinata (Ethiopian mustard), B. napus (rapeseed), B. juncea (oriental mustard), B. rapa (field mustard), and S. arvensis (wild mustard), were recovered by cold pressing and filtration without further refining. The physical, chemical, and lubricant properties of the oils were determined. B. napus had the highest oil yield when extracted by cold press and B. juncea* (low erucic B. juncea) had the highest oil content extracted by solvent. C. sativa oil had the lowest sterol content, mineral content, oxidative stability, and viscosity at 40 and 100°C and had the highest iodine value among tested oils. Iodine value had a strong negative correlation with oxidative stability in the tested oils. There was not any water in any of the oil samples. Overall, C. sativa oil had properties making it suitable for use as a fuel although its low oxidative stability and high iodine value could pose challenges; however, S. alba oil was more appropriate for use as a lubricant.
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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 | 0.001 | 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) | 0.001 | 0.000 |
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".