Effect of Manure on Glyphosate and Trifluralin Mineralization in Soil
Bibliographic record
Abstract
Manure additions to soil may alter soil chemical, physical, and biological characteristics, and thereby change pesticide fate processes in soil. This is the first study to examine the impact of liquid hog manure amendments on glyphosate and trifluralin mineralization in soil. Experiments were conducted in soil microcosms in the laboratory for a total of 332 (glyphosate) and 430 (trifluralin) days. The rate and amount of mineralization of both glyphosate and trifluralin were significantly influenced by the additions of fresh manure to soil in the laboratory and by the history of manure applications in the field. However, the maximum difference in herbicide mineralization between soils that were free of manure application and those amended with manure in the field or in the laboratory was only 6.1% and 7.3% of that initially applied, for trifluralin and glyphosate, respectively. Therefore, we conclude that liquid hog manure application to soil will have no significant effect on the mineralization of glyphosate and trifluralin under field conditions.
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 | 0.001 |
| 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".