Quantitation of fluoroquinolones in honey using tandem mass spectrometry (LC-MS/MS): nested validation with two mass spectrometers.
Bibliographic record
Abstract
A number of drugs in the quinolone and fluoroquinolone families, approved for veterinary treatment of food animals by various countries, may be used to treat bee diseases and thereby contaminate honey. An LC-MS/MS method has been developed for the quantification of the quinolones: flumequine, nalidixic acid, oxolinic acid, and pipemidic acid; and the fluoroquinolones ciprofloxacin, danofloxacin, difloxacin, enrofloxacin, norfloxacin, ofloxacin, orbifloxacin, marbofloxacin, sarafloxacin, and sparfloxacin. A method-matched calibration curve is used with several internal standards, i.e., ciprofloxacin-d8, Iomefloxacin, and cinoxacin, to correct for the various types of honey matrices: white, light, medium, and dark colors. Enoxacin is added as an external recovery standard. The LOD values range from 0.05 microg/kg (ofloxacin) to 0.4 microg/kg (flumequine). The compounds are verified by LC-MS/MS retention times and ion ratios. Method uncertainty was determined using two separate analytical systems. The method has successfully measured the presence of norfloxacin in several samples of honey imported into Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.002 | 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".