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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".