A Review of Methods for the Analysis of Orphan and Difficult Pesticides: Glyphosate, Glufosinate, Quaternary Ammonium and Phenoxy Acid Herbicides, and Dithiocarbamate and Phthalimide Fungicides
Bibliographic record
Abstract
This article reviews the chromatography/MS methodologies for analysis of pesticide residues of orphan and difficult chemical classes in a variety of sample matrixes including water, urine, blood, and food. The review focuses on pesticide classes that are not commonly included in multiresidue analysis methods such as highly polar or ionic herbicides including glyphosate, glufosinate, quaternary ammonium, and phenoxy acid herbicides, and some of their major degradation or metabolite products. In addition, dithiocarbamate and phthalimide fungicides, which are thermally unstable and have stability issues in some solvents or sample matrixes, are also examined due to their special needs in residue analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".