Bibliographic record
Abstract
This paper presents an ultra HPLC/electrospray ionization-tandem MS method to determine pesticides in wine. We adopted the quick, easy, cheap, effective, rugged, and safe (QuEChERs) method for extraction and used core-shell column to achieve ultra-HPLC to develop and validate a simple and fast method to analyze 187 pesticide residues in red and white wine samples. Pesticide residues were extracted from wine samples using QuEChERS. Ultra HPLC/electrospray ionization-tandem MS quantification was achieved using matrix-matched standard calibration curves with isotopically labeled standards or a chemical analogue as internal standards with an analytical range from 5.0 to 500.0 μg/L. The method performance characteristics that included overall recovery, intermediate precision, and measurement uncertainty were evaluated according to a nested experimental design. Generally, 98.4% (in red wine) and 96.8% (in white wine) of the pesticides had recoveries between 71 and 120%; 98.9% (in red wine) and 99.5% (in white wine) of the pesticides had the intermediate precision ≤20%; and 99.5% (in red wine) and 98.4% (in white wine) of the pesticides had measurement uncertainty ≤50%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".