Weed control in soybean using pyroxasulfone and sulfentrazone
Bibliographic record
Abstract
Belfry, K. D., McNaughton, K. E. and Sikkema, P. H. 2015. Weed control in soybean using pyroxasulfone and sulfentrazone. Can. J. Plant Sci. 95: 1199-1204. Pyroxasulfone and sulfentrazone are new herbicides currently being evaluated for weed control in soybean [Glycine max (L.) Merr.] in Ontario, Canada. Seven experiments were conducted over a 3-yr period (2011 to 2013) at Ridgetown and Exeter, Ontario, to evaluate weed management using pyroxasulfone, sulfentrazone and their tank-mixes relative to the industry standard, imazethapyr plus metribuzin. Tank-mixing pyroxasulfone and sulfentrazone provided up to 97, 46, 60, 100 and 71% control of common lambsquarters (Chenopodium album L.), common ragweed (Ambrosia artemisiifolia L.), green foxtail [Setaria viridis (L.) Beauv.], Powell amaranth [Amaranthus powellii (S.) Wats.] and velvetleaf (Abutilon theophrasti Medic.), respectively, at 2 wk after treatment. Control with pyroxasulfone and sulfentrazone was improved when tank-mixed, relative to application of each herbicide separately. Although control was variable across weed species, no difference in control was identified between pyroxasulfone plus sulfentrazone and imazethapyr plus metribuzin. Soybean yield was up to 2.7, 2.4 and 2.9 t ha-1 for pyroxasulfone, sulfentrazone and pyroxasulfone plus sulfentrazone application, yet imazethapyr plus metribuzin provided the highest yield (3.3 t ha-1). This research demonstrates that pyroxasulfone plus sulfentrazone may be used as a valuable weed control option in soybean; however, weed community composition may limit herbicidal utility.
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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) | 0.000 | 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".