Mineralisation and degradation of 2,4‐dichlorophenoxyacetic acid dimethylamine salt in a biobed matrix and in topsoil
Bibliographic record
Abstract
BACKGROUND: Biobeds are used for on-farm bioremediation of pesticides in sprayer rinsate and from spills during sprayer filling. Using locally sourced materials from Saskatchewan, Canada, a biobed matrix was evaluated for its effectiveness for mineralising and degrading 2,4-dichlorophenoxyacetic acid dimethylamine salt (2,4-D DMA) compared with the topsoil used in the biobed matrix. RESULTS: Applying 2,4-D DMA to the biobed matrix caused a 2-3 day lag in CO2 production not observed when the herbicide was applied to topsoil. Despite the initial lag, less residual 2,4-D was measured in the biobed (0%) matrix than in the topsoil (57%) after a 28 day incubation. When the herbicide was applied 5 times to the biobed matrix, net CO2 increased immediately after each 2,4-D DMA application. Mineralisation of 2,4-D DMA was 61.9% and residual 2,4-D in the biobed matrix was 0.3% after 60 days, compared with corresponding values of 32.9 and 70.9% in topsoil. CONCLUSION: The biobed matrix enhanced the mineralisation and degradation of 2,4-D DMA, indicating the potential for successful implementation of biobeds under Canadian conditions. The biobed matrix was more effective for mineralising and degrading the herbicide compared with the topsoil used in the biobed matrix. By correcting for biobed matrix and formulation blank, CO2 evolution was a reliable indicator of 2,4-D DMA mineralisation. © 2016 Society of Chemical Industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".