Bibliographic record
Abstract
A method was developed and validated for the determination of nitroimidazoles (NIs) including 2-hydroxymethyl-1-methyl-5-nitroimidazole (HMMNI), ipronidazole (IPZ), 1-methyl-2-(2'-hydroxyisopropyl)-5-nitroimidazole (IPZ-OH), metronidazole (MNZ), 1-(2-hydroxyethyl)-2-hydroxymethyl-5-nitroimidazole (MNZ-OH), ronidazole (RNZ) and dimetridazole (DMZ) in fish and crustaceans by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The compounds were extracted with acidic acetonitrile, followed by solid phase extraction (SPE) C18 clean up and hexane washing. The validated method has a linear range from 0-50 ng/g in target representative species (tilapia, salmon and shrimp), with LOD from 0.07 - 1.0 ng/g and LOQ from 0.21 - 3.0 ng/g, depending on the analyte. Recoveries ranged from 87 to 121% for the analytes of interest. Method precision ranged from 6 to 26% RSD with HorRat values within typical limits of acceptability. The method successfully analyzed rainbow trout samples treated with MNZ in a depletion study under controlled conditions and is suitable for use in a regulatory monitoring program for residues of nitroimidazoles in seafood and aquacultured products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".