Organophosphorus Pesticide Extraction and Cleanup from Soils and Measurement Using GC-NPD
Bibliographic record
Abstract
The objectives of this study were to optimize instrumental parameters and conditions for analysis of selected organophosphorus pesticides (OPPs) by gas chromatography (GC) with nitrogen-phosphorus detection (NPD) (GC-NPD); to select an appropriate solvent system; to conduct a comparison of sonication and shaking extractions; and to select an appropriate procedure for extracting organophosphorus pesticides from soils. Procedure Ⅰ consisted of n-hexane or petroleum ether together with acetone used as solvents, while Procedure Ⅱ contained several solvents including acetone,methanol, dichloromethane, and n-hexane or petroleum ether. Experimental results indicated that a mixture of petroleum ether/acetone (2:1, v/v) could be used in place of n-hexane/acetone (2:1, v/v) as it was a less expensive solvent system.In addition, shaking under a water bath at 20 ℃ was more effective than sonication. Also, Procedure Ⅰ was more effective,safer, and more timesaving than Procedure Ⅱ. Procedure I was applied to three soil types of different organic matter content, with recoveries of the OPPs from the yellow-brown soils, which had a higher organic matter content, being lower than those from the yellow and red soils.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".