Sorption of atrazine and metolachlor by earthworm surface castings and soil
Bibliographic record
Abstract
Atrazine and metolachlor were more strongly retained on earthworm (Lumbricus terrestris L.) castings than on soil, suggesting that earthworm castings at the surface or at depth can reduce herbicide movement in soil. Herbicide sorption by castings was related to the food source available to the earthworms. Both atrazine and metolachlor sorption increased with increasing organic carbon (C) content in castings, and Freundlich constants (Kf values) generally decreased in the order: soybean-fed > corn-fed > not-fed-earthworm-castings. The amount of atrazine or metolachlor sorbed per unit organic carbon (Koc values) was significantly greater for corn-castings compared with other castings, or soil, suggesting that the composition of organic matter in castings is also an important factor in determining the retention of herbicides in soils. Herbicide desorption was dependent on both the initial herbicide concentration, and the type of absorbent. At small equilibrium herbicide concentrations, atrazine desorption was significantly greater from soil than from any of the three casting treatments. At large equilibrium herbicide concentrations, however, the greater organic C content in castings had no significant effect on atrazine desorption, relative to soil. For metolachlor, regardless of the equilibrium herbicide concentration, desorption from soybean- and corn-castings treatments was always less than desorption from soil and not-fed earthworm castings treatments. The results of this study indicate that, under field conditions, the extent of herbicide retention on earthworm castings will tend to be related to crop and crop residue management practices.
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".