Tolerance of Soybean (<i>Glycine max</i> L.) to Protoporphyrinogen Oxidase Inhibitors and Very Long Chain Fatty Acid Synthesis Inhibitors Applied Preemergence
Bibliographic record
Abstract
Nine field experiments were conducted in 2011 and 2012 at various locations in southern Ontario, Canada to determine the tolerance of soybean (Glycine max (L.) Merr.) to herbicides inhibiting protoporphyrinogen oxidase (Protox) and very long chain fatty acid (VLCFA) synthesis applied alone and in combination. Preemergence applications were evaluated for soybean injury, plant height, shoot dry weight, and yield in the absence of weed competition. Early-season soybean injury from the Protox inhibitors persisted 4 weeks after soybean emergence (WAE) with 3%, 5%, and 18% injury for flumioxazin, saflufenacil, and sulfentrazone, respectively. When Protox inhibitors were tank mixed with VLCFA inhibitors (i.e., dimethenamid-P, S-metolachlor, and pyroxasulfone), additive interactions were observed for injury with saflufenacil and sulfentrazone; whereas synergistic interactions were observed with flumioxazin. However, injury subsided over time decreasing from as much as 34% injury 1 WAE for the flumioxazin + S-metolachlor tank mix down to 9% injury 4 WAE. In general, when saflufenacil or flumioxazin were tank mixed with VLCFA inhibitors, greater than expected reductions in height and dry weight were observed indicating synergistic responses; while no interactive effects were detected with sulfentrazone and VLCFA inhibitor tank mixes. For the flumioxazin tank mixes that contained dimethenamid-P or S-metolachlor, the reduction in yield was greater than expected indicating synergistic interactive effects. Yet, all the demonstrated impacts were transient as the yield for soybean treated with any of the Protox inhibitor and VLCFA inhibitor tank mixes tested were similar to the untreated control. Therefore, usage restriction on these mixtures, based on perceived negative yield impact, should be lifted so the herbicides could be combined to expand weed control options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".