Chlorothalonil and Its 4-Hydroxy Derivative in Simple Quartz Sand Soils: A Comparison of Sorption Processes
Bibliographic record
Abstract
Quartz sandy soils from Simcoe, Ontario, Canada and North Carolina had sorption properties for chlorothalonil that were nearly the same. For labile surface sorption kinetics, the Simcoe soil gave a pseudo first-order rate constant of kS1 = (7.4 +/- 0.7) x 10(-2) days-1. At equilibrium, the labile surface sorption capacity theta c of Simcoe soil for chlorothalonil was 23.8 x 10(-6) (mol/g). The sorption properties of the 4-hydroxy derivative of chlorothalonil were different in two important respects. They were larger by an order of magnitude, and they were substantially different for the two soils. Sorption by the Simcoe soil was too fast for kinetics measurements by the on-line HPLC micro extraction method, but for the North Carolina soil kS1 = (1.15 +/- 0.01) days-1 was recorded. For the Simcoe and North Carolina soils, respectively, theta c > 200 (mumol/g) and theta c approximately 113 (mumol/g). Two conclusions can be drawn. First, the replacement of the Cl by OH on the 4 position of chlorothalonil makes the sorption effects much greater. Second, the stronger interactions are associated with a greater sensitivity to small differences in the chemical compositions of the soils. Subtle soil properties causing significant effects might include small amounts and physical structures of organic matter and metal oxides. This implies that, for predictive computer models, mechanism parameters will have to be correlated in two dimensions: chemical structure, and the composition and amounts of chemical materials in 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".