Degradation and residue dynamics of fluazinam in diverse indian soil types and water pH conditions: a comprehensive study using kinetic models
Bibliographic record
Abstract
Fluazinam a promising fungicide, is not yet registered in India. Consequently it is important to study the dissipation of its specific formulation in Indian soil and water. This study focuses on the degradation and residue dynamics of Fluazinam (40% SC) in different soil types (alluvial, lateritic, coastal saline and black) and water pH (4.0, 7.0, 9.2). Adsorption kinetic models suggested that the half-life period (days) varies among soils following the order lateritic (Jhargram), 54.07 > alluvial (Mohanpur), 45.10 > coastal saline (Canning), 28.33 > black (Pune) 26.18. These differences are attributed to soil pH and organic carbon (OC) content, where higher pH levels reduce pesticide adsorption, leading to quicker dissipation, while higher organic carbon content provides more binding sites, slowing down the process. The first order kinetics explained the dissipation better compared to second order model across all soil types. The study also found that the half-life of was lowest at pH 9.2, as compared to pH 7.0, and very high stability at pH 4.0. Additionally, the study introduces an interactive R-based tool for analysing dissipation kinetics and half-life of different pesticides offering a valuable resource for researchers and stakeholders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".