Additive efficacy of soil-applied pyroxasulfone and sulfentrazone combinations
Bibliographic record
Abstract
Tidemann, B. D., Hall, L. M., Johnson, E. N., Beckie, H. J., Sapsford, K. L., Willenborg, C. J. and Raatz, L. L. 2014. Additive efficacy of soil-applied pyroxasulfone and sulfentrazone combinations. Can. J. Plant Sci. 94: 1245-1253. Efficacy of soil-applied herbicides can be influenced by edaphic factors including soil organic matter (OM) content, as well as by interactions with herbicide tank-mix partners. Field trials were conducted over 6 site-years in 2011 and 2012 across western Canada to examine the interaction of pyroxasulfone and sulfentrazone when co-applied for control of false cleavers (Galium spurium L.) and wild oat (Avena fatua L.) in field pea. In the greenhouse, the nature of this interaction was further investigated for these two weed species, plus barley and canola; in a separate experiment, the effect of OM content on pyroxasulfone and sulfentrazone efficacy was examined using three soils with 2.8, 5.5, and 12.3% OM content, respectively. Efficacy of pyroxasulfone and sulfentrazone combinations was additive under both field and greenhouse conditions. Higher OM content generally required higher rates of herbicide to achieve similar efficacy for all tested species. Pyroxasulfone and sulfentrazone can be combined to aid in herbicide resistance management and broaden the weed spectrum compared with each product used alone, although rate selection may be OM dependent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".