Effect of Temperature on the Dissipation of Seven Herbicides in a Biobed Matrix
Bibliographic record
Abstract
Cold winters and short, warm summers in the Canadian prairies pose a challenge for the effectiveness of on-farm biobeds for degrading agricultural pesticides. A thermo-gradient plate was used to evaluate the effect of temperature on the dissipation kinetics of seven commonly used herbicides applied to a biobed matrix composed of materials typically available on a farm. The dissipation of all seven herbicides increased with increasing incubation temperature and duration. 2,4-D, bromoxynil, and thifensulfuron-methyl dissipated completely during the 35 d incubation at 13 and (or) 20 °C. Tribenuron-methyl, pyrasulfotole, thiencarbazone-methyl, and metsulfuron-methyl dissipated 93%, 70%, 64%, and 34%, respectively, at 20 °C. The order of decreasing dissipation in the biobed matrix reflected the relative soil half-lives and soil sorption coefficients of the herbicides. Metsulfuron-methyl and thiencarbazone-methyl had the lowest activation energies and temperature quotients and were the least sensitive to increases in incubation temperature. At 20 °C, the half-lives of all herbicides were <70 d. However, 10 yr average soil temperatures to 1 m depth from a site in Saskatoon, SK, were considerably <20 °C for much of the growing season. Assuming soil temperatures to be a proxy for expected temperatures of on-farm biobeds, biobed temperatures would not be high enough for a long enough period of time to achieve complete dissipation of some herbicides. Consequently, biobeds in the Canadian prairie provinces may require supplemental heating, especially in spring and late fall, to maintain incubation temperatures of approximately 20 °C to optimize the degradation of herbicides used in prairie crop production.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".