Comparison of gas chromatography-mass spectrometry and gas chromatography-tandem mass spectrometry with electron ionization and negative-ion chemical ionization for analyses of pesticides at trace levels in atmospheric samples. Anal Chem Insights
Bibliographic record
Abstract
Abstract: A comparison of detection limits of gas chromatography-mass spectrometry (GC-MS) in selected ion monitoring (SIM) with gas chromatography-tandem mass spectrometry (GC-MS/MS) in selected reaction monitoring (SRM) mode with both electron ionization (EI) and negative-ion chemical ionization (NCI) are presented for over 50 pesticides ranging from organochlorines (OCs), organophosphorus pesticides (OPs) and pre-emergent herbicides used in the Canadian prairies (tri-allate, trifl uralin, ethalfl uralin). The developed GC-EI/SIM, GC-NCI/SIM, and GC-NCI/SRM are suitable for the determina-tion of pesticides in air sample extracts at concentrations 100 pg µL−1 (100 pg m−3 in air). No one method could be used to analyze the range of pre-emergent herbicides, OPs, and OCs investigated. In general GC-NCI/SIM provided the lowest method detection limits (MDLs commonly 2.5–10 pg µL−1) along with best confi rmation (25 % RSD of ion ratio), while GC-NCI/SRM is recommended for use where added selectivity or confi rmation is required (such as parathion-ethyl, toku-thion, carbofenothion). GC-EI/SRM at concentration 100 pg µL−1 was not suitable for most pesticides. GC-EI/SIM was
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".