Application of Automated Mass Spectrometry Deconvolution and Identification Software for Pesticide Analysis in Surface Waters
Bibliographic record
Abstract
A new approach to surface water analysis has been investigated in order to enhance the detection of different organic contaminants in Nathan Creek, British Columbia. Water samples from Nathan Creek were prepared by liquid/liquid extraction using dichloromethane (DCM) as an extraction solvent and analyzed by gas chromatography mass spectrometry method in scan mode (GC-MS scan). To increase sensitivity for pesticides detection, acquired scan data were further analyzed by Automated Mass Spectrometry Deconvolution and Identification Software (AMDIS) incorporated into the Agilent Deconvolution Reporting Software (DRS), which also includes mass spectral libraries for 567 pesticides. Extracts were reanalyzed by gas chromatography mass spectrometry single ion monitoring (GC-MS-SIM) to confirm and quantitate detected pesticides. Pesticides: atrazine, dimethoate, diazinone, metalaxyl, myclobutanil, napropamide, oxadiazon, propazine and simazine were detected at three sampling sites on the mainstream of the Nathan Creek. Results of the study are further discussed in terms of detectivity and identification level for each pesticide found. The proposed approach of monitoring pesticides in surface waters enables their detection and identification at trace levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".