Effect of soil organic amendments on sorption behavior of two insecticides and two herbicides
Bibliographic record
Abstract
The effect of biochar, compost, peat and wheat straw at 1 and 5% on adsorption isotherm of chlorantraniliprole, dinotefuran, bispyribac-sodium, and metribuzin was studied in clay loam soil and sandy loam soil. Biochar, compost, peat and wheat straw (at a rate of 1 % in soil) improved the adsorption capacity of chlorantraniliprole and metribuzin in sandy loam soil. The sorption coefficients are higher for chlorantraniliprole and metribuzin whereas lower for dinotefuran and bispyribac-sodium in amended soil compared to unamended sandy loam soil. There is not a clear direct correlation between Freundlich parameters as well as Kd or Koc and type of organic amendment. The sorption of all tested pesticides on biochar was increased, whereas on compost was decreased. The order of pesticides sorption in soils and different organic amendments is generally inversely proportional to their aqueous solubilites. Adsorption of chlorantraniliprole increases on the sandy loam soil amendment at the rate of 1% in the following order: peat > compost > biochar > original soil. Also, the magnitude of adsorption on soil A amendment at the rate of 5% can be arranged for dinotefuran in the order; peat > biochar > compost > original soil and for bispyribac-sodium and metribuzin peat = wheat straw > biochar > original soil.
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 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) | 0.001 | 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".