Determination of Decoquinate in Animal Feeds by Liquid Chromatography: Collaborative Study
Bibliographic record
Abstract
The performance characteristics of a liquid chromatographic (LC) method for the analysis of decoquinate (DEC) in supplements, premixes, and complete animal feeds at medicating and trace levels were collaboratively studied. DEC is extracted from ground feed samples with 1% calcium chloride-methanol solution using mechanical agitation for 90 min. After centrifugation for 5 min and dilution (if necessary), an aliquot of the extract is diluted with water. The diluted extracts are filtered and analyzed by reversed-phase LC with fluorescence detection. Suspect positive trace-level samples are confirmed by using an alternate excitation wavelength. Fourteen test samples of medicated feeds, supplement, and medicated premix, along with 8 test samples for trace-level analysis, were sent to 13 collaborators (one in Canada, 4 in Europe, and 8 in the United States). Test samples were analyzed as blind duplicates. Acceptable results were received from 12 laboratories for the medicated test samples and from 13 laboratories for the trace-level samples. Repeatability relative standard deviation estimates ranged from 1.3 to 5.6%. Reproducibility relative standard deviations estimates ranged from 2.8 to 6.1%, and HorRat values ranged from 0.22 to 0.74.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".