Ultrasound‐assisted extraction of radish seed oil with methyl acetate for biodiesel production
Bibliographic record
Abstract
Abstract This study evaluated the extraction of radish seed oil (Raphubus sativus L.) for application in biodiesel production. The experiments were performed in a process assisted by ultrasound using methyl acetate as the solvent, seeking to evaluate the effects of the process parameters, such as time, temperature, and solvent‐to‐seed ratio, on the oil yield, and to establish the conditions that maximize the oil extraction yield. Conventional extraction was performed for comparative purposes. The extraction time had the greatest influence on the ultrasound‐assisted extraction (UAE) for the experimental range evaluated, followed by the temperature and amount of solvent. The maximum oil recovery (33 %) was obtained for the experiment conducted at 60 °C and a solvent‐to‐seed ratio of 10 mL/g with an extraction time of 90 min. The application of ultrasound influenced the oil yield and at 120 min a yield of 33 % was obtained. Oleic and erucic acids were the main fatty acids identified in the radish seed oil. The fatty acid composition and physico‐chemical characteristics of the oils obtained were not influenced by the extraction method, but the use of ultrasound had a stronger influence on the removal of phytosterols.
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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 | 0.000 | 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 | 0.000 |
| 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".