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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".