Effect of Operating Parameters on Oil and Phenolic Extraction Using Supercritical CO<sub>2</sub>
Bibliographic record
Abstract
Abstract Supercritical fluid extraction (SFE) with carbon dioxide was used to extract oil from canola press cake. Different operating conditions, e.g. pressure, temperature, and co‐solvent % were investigated to optimize extraction parameters to yield canola meal with <4% oil. The residual oil content in the extracted canola meal reduced to 2.1–2.9% in our experimental trials. Residues of the optimum conditions based on oil yield were compared for the total phenolic content and the main phenolic compounds. Sinapine (the choline ester of sinapic acid) was the major phenolic constituent in both the SFE and n ‐hexane extracted canola meals and press cakes. n ‐Hexane extracted residues showed the retention of the highest sinapic acid, sinapine, sinapoyl glucose and total phenolic contents (mg/g) while the SF‐extracted residues showed the lowest values for these compounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".