Production and certification of BOTS-1: bovine muscle–certified reference material for incurred veterinary drug residues
Bibliographic record
Abstract
Abstract A freeze-dried bovine muscle–certified reference material (CRM), known as BOTS-1 (DOI: https://doi.org/10.4224/crm.2018.bots-1 ), containing incurred residues of commonly used veterinary drugs was produced and certified for the mass fraction of eight veterinary drug residues. Value assignment was carried out using liquid chromatography tandem mass spectrometry (LC–MS/MS) methods in conjunction with isotope dilution and standard addition approaches involving stable isotope internal standards. Data from the National Research Council of Canada (NRC), Canadian Food Inspection Agency (CFIA), United States Department of Agriculture (USDA), and the Federal Office of Consumer Protection and Food Safety in Germany (BVL) were used for value assignment. Results for two drug residues were also obtained through an international inter-laboratory comparison CCQM-K141/P178 organized under the auspices of the International Bureau of Weights and Measures (BIPM). Quantitative NMR (1H-qNMR) was used to characterize primary standards of all veterinary drugs certified. The certified mass fractions of the veterinary drug residues were 490 ± 100 µg/kg for chlorpromazine, 44 ± 4.4 µg/kg for ciprofloxacin, 3.3 ± 1.4 µg/kg for clenbuterol, 9.5 ± 0.8 µg/kg for dexamethasone, 57 ± 4.8 µg/kg for enrofloxacin, 3.0 ± 0.4 µg/kg for meloxicam, 12.4 ± 1.2 µg/kg for ractopamine, and 2290 ± 120 µg/kg for sulfadiazine with expanded uncertainties quoted (95% confidence) which include the effects due to between-bottle inhomogeneity, instability during long-term storage and transportation, and characterization.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".