An integrated weed management strategy for the control of horseweed (<i>Conyza canadensis</i>)
Bibliographic record
Abstract
Abstract Multiple herbicide-resistant populations of horseweed [ Conyza canadensis (L.) Cronquist] continue to spread rapidly throughout Ontario, notably in areas where no-till soybean [Glycine max (L.) Merr.] is grown. The occurrence of multiple herbicide resistance within these populations suggests that the future role of herbicide tank mixtures as a means of control will be limited. An integrated weed management strategy utilizing complementary selection pressures is needed to reduce the selection intensity of relying solely on herbicides for control. Field studies were conducted in 2018 and 2019 to test the hypothesis: if fall-seeded cereal rye ( Secale cereale L.) can reduce C. canadensis seedling density and suppress seedling growth, then the interaction(s) of complementary selection pressures of tillage, cereal rye, and herbicides would improve the level of C. canadensis control. Laboratory studies were conducted to determine whether the allelopathic compound 2-benzoxazolinone (BOA) affected the root development of C. canadensis seedlings. The interactions observed among multiple selection pressures of tillage, cereal rye, and herbicides were inconsistent between the 2 yr of study. A monoculture of cereal rye seeded in the fall, however, did reduce seedling height and biomass of C. canadensis consistently, but not density. This reduction in seedling height and biomass was likely caused by the allelopathic compound BOA, which reduced seedling root development. Control of C. canadensis seedlings in the spring required the higher registered rates of dicamba or saflufenacil. The addition of shallow fall tillage and the presence of cereal rye did not improve the variability in control observed notably with 2,4-D or the lower rates of saflufenacil or dicamba. With the implementation of complementary weed management strategies, environmental variables in any given year will likely have a direct influence on whether these interactions are additive or synergistic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".