Control of herbicide-resistant common waterhemp (<i>Amaranthus tuberculatus</i> var. <i>rudis</i>) with pre- and post-emergence herbicides in soybean
Bibliographic record
Abstract
Common waterhemp was first identified in Ontario in 2002. Eight trials were conducted ove r a 3-yr period (2003-2005) at Cottam and Petrolia, Ontario to determine the efficacy of various preemergence and postemergence herbicides for the control of herbicideresistant waterhemp in soybean [ Glycine max (L.) Merr.]. Waterhemp was resistant to the a cetolactate synthase-inhibiting herbicides at both locations and to the photosystem II-inhibiting herbicides at Petrolia; therefore, results varied by location. Results at Petrolia also varied across years. Waterhemp interference resulted in soybean yield losses of up to 73% in weedy versus weed-free checks. Preemergence-applied metolachlor, dimethenamid, and linuron provided greater than 80% control and metolachlor plus metribuzin 94% or greater control of waterhemp 70 d after emergence at both locations. Acifluorfen, fomesafen, imazamox plus fomesafen, and glyphosate applied postemergence resulted in 87% or greater waterhemp control 70 d after application at Cottam in 2003 and 2004 and at Petrolia in 2004; however, waterhemp control with these herbicides was only 60 to 66% at Petrolia in 2005 due to prolonged weed emergence. Multiple herbicide applications per season may be required for effective waterhemp control in soybean. Key words:
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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) | 0.000 | 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 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".