Physical characteristics and Freundlich model of adsorption and desorption isotherm for fipronil in six types of Egyptian soil
Bibliographic record
Abstract
The soil type and temperature are considered important parameters that can influence the rates and equilibria of different environmental processes. Therefore, the adsorption and desorption isotherms of fipronil in clay loam, clay, sandy loam, sandy clay loam, sand and loamy sand soils at 25 and 50˚C was studied. The amount of fipronil adsorbed and desorbed by different soils was significantly influenced by the temperature. Adsorption was higher in clay loam, clay, sandy clay loam and sandy soil at 25°C, while sand soil and loamy sand soil at 50°C. The non-desorbed amount was greater at 25°C in different types of soil except for clay loam soil. The negative ΔG˚ indicated that the adsorption/desorption in different types of soil was spontaneous at different temperatures. The value of standard enthalpy change (ΔH˚) was positive in clay soil, sandy loam soil, sandy clay loam soil and loamy sand soil for adsorption and sandy loam soil, sand soil and loamy sand soil for desorption. Moreover, the standard entropy change (ΔS˚) was negative in soils for adsorption and desorption isotherms except clay loam soil. Adsorption and desorption isotherms trends as well as the values of the correlation coefficients indicated that the adsorption and desorption isotherms of fipronil in tested soils were fitted to the Freundlich model because the correlation coefficient is very close to 0.999.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".