Elucidating mechanistic interactions of pesticides with soil using nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
The interactions of pesticides with soil at the molecular level are central to their bioavailability, bioaccumulation, transport and toxicity in the environment. Elucidation of the mechanistic interactions of pesticides is critical to understanding and eventually predicting their behavior in the environment. Here, saturation transfer double difference (STDD) nuclear magnetic resonance (NMR) spectroscopy, is employed with both solution sate and high resolution magic angle spinning (HR MAS) NMR to determine the mechanistic interactions of pesticides with humic acid (HA) and whole-soil at a molecular level. HR-MAS is also used to obtaining information regarding physicochemical factors which influence sorption. The results suggest that electronegativity and electron density play a key role in the mechanism of pesticide binding, and the predominant modes of sorption are dipole-dipole interactions, H-bonding and pi-pi interactions. Physiochemical parameters such as background electrolyte and soil moisture content, which influence soil conformation are also shown to affect sorption mechanisms.
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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 | 0.000 |
| 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) | 0.002 | 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".