Evaluation of Bromoxynil, Pyraflufen-ethyl/2,4-D, and Tiafenacil for the Control of Glyphosate-resistant Canada fleabane (Conyza canadensis) in Soybean (Glycine max) and Metribuzin for the Control of Waterhemp (Amaranthus tuberculatus) with Two Mechanisms of Resistance to Photosystem II-inhibiting herbicides
Bibliographic record
Abstract
Fifteen field experiments were conducted in 2019 and 2020 to evaluate bromoxynil, pyraflufen-ethyl/2,4-D, and tiafenacil, alone and tankmixed with metribuzin, for the control of glyphosate-resistant (GR) Canada fleabane applied preplant in soybean. The biologically-effective-dose (BED) of bromoxynil +/- metribuzin, pyraflufen-ethyl/2,4-D +/- metribuzin, and tiafenacil +/- metribuzin was determined. Bromoxynil, pyraflufen-ethyl/2,4-D, and tiafenacil, tankmixed with metribuzin, provided similar control to the current industry standards at 8 weeks after application (WAA). Ten field and eight greenhouse experiments were conducted to evaluate metribuzin, applied preemergence (PRE) and postemergence (POST), for the control of photosystem II (PS II)-inhibitor resistant waterhemp conferred by target-site resistance (TSR) and non-target-site resistance (NTSR) mechanisms. In both the field and greenhouse studies NTSR PS II-inhibitor resistant waterhemp was controlled with metribuzin applied PRE and POST, in contrast TSR PS II-inhibitor resistant waterhemp was not controlled with metribuzin.
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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".