A Quantitative LC-MS/MS Study of the Partitioning, Transport, and Fate of Pesticide Residues on Soil
Why this work is in the frame
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Bibliographic record
Abstract
Titration of pesticides onto sorption sites can determine sorption capacities on soils. Previous studies have tracked the sorption capacities and detailed kinetics of the uptake of atrazine and its decomposition byproduct hydroxyatrazine on different soils, including measurements made using LC-MS/MS. These studies have now been extended to explore sorption-desorption equilibria for a mixture of pesticides from soil using LC-MS/MS. Desorption of sorbed pesticide residues has environmental regulatory implications for pesticide levels in runoff, or for longer term sequestration, partitioning, and transport. The uptake of pesticides by the soil at equilibrium was measured for a number of different concentrations, and sorption capacities were estimated. Pesticide-soil interaction studies were conducted by exposing standard stock solutions of pesticide mixtures to a characterized Nova Scotia soil. The mixture contained atrazine and dicamba. Initial aqueous mixture concentrations ranging from 5 × 10-9 to 10-5 M or greater were exposed to 25 mg aliquots of soil and allowed to reach equilibrium. The total uptake of each pesticide was measured indirectly, by measuring the concentration remaining in solution using an IONICS 3Q 120 triple quadrupole mass spectrometer. These sorption capacities have been supplemented by studies examining equilibrium recovery rates from soil aliquots with different initial uptakes. This gives insight into the fraction of easily recoverable (reversibly sorbed) pesticides on the soil. Proper quantification of equilibrium constants and kinetic rate coefficients using high performance LC-MS/MS facilitates the construction of accurate, predictive models. Predictive kinetic models can successfully mimic the experimental results for solution concentration, labile sorption, and intra-particle diffusion, and could be used to guide regulatory practices.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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