Modeling study of adsorption isotherms of chlorantraniliprole and dinotefuran on soil
Bibliographic record
Abstract
Knowledge of pesticide adsorption characteristics is essential to predict their behavior in soil. The adsorption equilibrium isotherms of two insecticides chlorantraniliprole (CAP) and dinotefuran (DNF) on two common Egyptian soil types, clay loam and sandy loam were studied and modeled. To predict the adsorption isotherms and to determine the adsorption parameters, ten isotherm models: Langmuir (five linear forms), Freundlich, Temkin, Dubinin-Radushkevich, Elovick, Fowler-Guggenheim, Kiselev, Jovanoic, Harkins-Jura, and Halsey were applied on experimental data. The results revealed that the adsorption isotherm models fitted the data in the order of Halsey > Freundlich > Jovanoic > Langmuir isotherme. The models of Harkins-Jura, Elovich, Temkin, and Fowler-Guggenheim are not applicable to predict the adsorption isotherms of the tested insecticides. In order to determine the best-fit isotherm, the correlation coefficient (R2), comparing the experimental (exp) and calculated (cal) adsorption data, and a normalized standard deviation (Δg%) were used to evaluate the data. Therefore, the isotherm models Halsey and Freundlich could be used to predict the adsorption characteristics of CAP and DNF in the common Egyptian soil types, clay loam and sandy loam. Consequently, the mathematical models Halsey, Freundlich, and Jovanoic can describe the fate of CAP and DNF and can be used to control Egyptian soil contamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".