The Comparison Extraction Methods of Crude Fat Content and Fatty Acid Profile of Eels (Anguilla marmorata (Q.) Gaimard) from Lake Poso
Bibliographic record
Abstract
Eels (Anguilla marmorata (Q). Gaimard) from Lake Poso contain nutrition such as fatty acids EPA and DHA that are good for health. Extraction processes can be carried out to get the fats of these eels (Anguilla marmorata). This research aims to determine which one of the following extraction methods, namely: maceration and soxhletation, which is more appropriate to extract eels (Anguilla marmorata) so as to produce crude fats and the fatty acid profile with a higher content. The fatty acid profile was examined using the method of gas chromatography by converting results of the fat extraction into volatile fatty acid methyl esters. Research findings suggest that the obtained mean of crude fats was equal to 3.704% for the method of maceration and 28.872% for the method of soxhletation. Furthermore, results of the statistical testing analysis of the fatty acid profile generated a sig. a value greater than the 5% significance level, meaning that the content of fatty acids generated using the extraction method of maceration and that generated using the extraction method of soxhletation were not significantly different. This can be seen from the means of the fatty acid content, namely by 4.6956%, 9.6496%, and 1.5765% for the maceration method, and by 4.7287%, 9.338%, and 1.6646% for the soxhletation method. Based on the foregoing, it can be concluded that soxhletation is the appropriate extraction method.
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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 | 0.000 |
| 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) | 0.000 | 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 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".