Inter-laboratory validation of automated SPME-GC/MS for determination of pesticides in surface and ground water samples: sensitive and green alternative to liquid–liquid extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An automated solid-phase microextraction gas chromatography/mass spectometry (SPME-GC/MS) method was developed for the determination of semi-volatile pesticides from several classes with a wide range of polarities in an environmental matrix, and validated according to the rigorous standards of a large commercial laboratory reporting data requiring regulatory acceptance with the purpose of being used as a standard test protocol. The target analytes showed a detection limit of 0.05–1 μg L−1, good calibration linearity (R2 > 0.99) with a wide linear range of 0.05–20 μg L−1, and accuracy in the range of 80–110 at three levels of calibration with relative standard deviation below 7% by commercial polydimethylsiloxane/divinylbenzene (PDMS/DVB) SPME fiber. An extensive study between SPME and liquid–liquid extraction as a reference US EPA method was performed from several analytical aspects including sensitivity, accuracy, repeatability, and greenness. The SPME method was validated through split blind analyses of 16 fortified surface and ground water samples within 4 months at Maxxam Analytics, the reference laboratory, and the University of Waterloo. Both methods were shown to be very accurate, with the highest frequency of results falling in the 70–130% accuracy range. The SPME method was shown to be more sensitive than the LLE, while requiring a lower volume of sample.
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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 | 0.006 | 0.001 |
| 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 it