Analysis of Orphan and Difficult Herbicides and/or Pesticides
Bibliographic record
Abstract
Abstract This article reviews the main chromatography/mass spectrometry methods for the analysis of four main categories of herbicides and related pesticides including cationic charged herbicides (quaternary ammonium (quat) herbicides), anionic charged herbicides or organophosphorus herbicides (glyphosate (GLYP) and glufosinate (GLUF)), sulfonylurea herbicides that utilize liquid chromatography‐positive ion electrospray ionization‐tandem mass spectrometry (LC‐ESI+‐MS/MS), and phenoxyacid herbicides that have optimal mass spectrometry (MS) sensitivity with liquid chromatography‐negative ion electrospray ionization‐tandem mass spectrometry (LC‐ESI−‐MS/MS). In addition, selective degradation products of these herbicides that have been analyzed by chromatography‐mass spectrometry methods will be reviewed. Selected pesticides that are frequently simultaneously analyzed in targeted class‐specific methods for these four chemical classes of herbicides will be further discussed. The focus of this review is on the chromatography‐mass spectrometric detection methods for these classes of herbicides with priority given to liquid chromatography (LC)‐tandem mass spectrometry methods. These methods apply to the analysis of herbicides in food and environmental samples although sample preparation or clean‐up requirements may vary. The review will provide an overview to users in the best currently available chromatography‐mass spectrometry methods for these targeted chemical classes with an approach to include a broad range of herbicides within the class for future diversity in method development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".