Crude Fat, Diethyl Ether Extraction, in Feed, Cereal Grain, and Forage (Randall/Soxtec/Submersion Method): Collaborative Study
Bibliographic record
Abstract
A method for determining crude fat in animal feed, cereal grain, and forage (plant tissue) was collaboratively studied. Crude fat was extracted from the animal feed, cereal grain, or forage material with hexanes by the Randall method, also called the Soxtec method or the submersion method. The use of hexanes provides for an alternative to diethyl ether for fat extractions. The proposed submersion method considerably decreases the extraction time required to complete a batch of samples compared to Soxhlet. The increase in throughput is very desirable in the quest for faster turnaround times and the greater efficiency in the use of labor. In addition, this method provides for reclamation of the solvent as a step of the method. The submersion method for fat extraction was previously studied for meat and meat products and was accepted as AOAC Official Method 991.36. Fourteen blind samples were sent to 14 collaborators in the United States, Sweden, Canada, and Germany. The within-laboratory relative standard deviation (repeatability) ranged from 1.23 to 5.80% for crude fat. Among-laboratory (including within) relative standard deviation (reproducibility) ranged from 1.88 to 14.1%. The method is recommended for Official First Action.
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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 | 0.013 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".