Determination of Clopidol Residues in Chicken Tissues by Liquid Chromatography: Collaborative Study
Bibliographic record
Abstract
Eighteen laboratories participated in a collaborative study on the determination of clopidol residues in chicken muscle tissues by liquid chromatography. Of these, results from 16 laboratories which rigorously followed the method were subjected to statistical analysis. The method performance was assessed by all participants using 14 samples of chicken muscle fortified at concentrations ranging from 0.1 to 5.0 mg/kg. In addition, 9 participants each reported results for 6 clopidol-incurred samples in chicken muscle. Test portions were extracted with acetonitrile, and the extracts were purified with alumina and anion exchange resin solid-phase extraction cartridges in sequence. Clopidol was separated by reversed-phase liquid chromatography and quantified at 270 nm. Average recoveries ranged from 81.8 to 85.4%, reproducibility relative standard deviation (RSDR) ranged from 11.9 to 22.6%, and repeatability relative standard deviation (RSDr) ranged from 9.9 to 15.1%. For clopidol-incurred samples at concentrations of 0.100-0.687 mg/kg, the mean determination value range was 0.099-0.659 mg/kg; RSDR was 12.6-19.8%, RSDr was 3.1-8.5%; and HORRAT values were 0.7-1.1. The accuracy and precision of the method are in conformity with the requirements specified by AOAC INTERNATIONAL. The method was adopted Official First Action in April 2003.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".