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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
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".