Effect of Seeding Rate on Dose Response of Wild Mustard (<i>Sinapis arvensis</i>) to Fluthiacet-Methyl
Bibliographic record
Abstract
Concern over the development of herbicide-resistant weeds has led to interest in integrated weed management systems that reduce selection pressure by utilizing mechanical and cultural weed control practices in addition to herbicides. Increasing crop seeding rate increases crop competitive ability and thus can enhance herbicide efficacy. However, it is unknown how increasing the seeding rate affects an herbicide’s efficacy. The objective of this study was to examine the interaction between increasing seeding rate and herbicide dose to control weeds. To meet this objective, the herbicide fluthiacet-methyl was applied to field-grown lentil, with Indian mustard, a proxy for wild mustard, used as a model weed. The experiment was a factorial design with four lentil seeding rates and seven herbicide rates. Overall the herbicide dose response was altered by changing lentil seeding rate. Increasing lentil seeding rate decreased the weed biomass production when herbicides were not applied. In two of the four site-years, increasing lentil seeding rate lowered the herbicide ED50, the dose required to result in a 50% reduction in weed biomass. Increasing the crop seeding rate altered the dose response to provide greater weed control at lower herbicide rates compared with normal crop seeding rates. Increased seeding rates also resulted in higher and more stable crop seed yields across a wider range of herbicide dosages. These results suggest that dose–response models can be used to evaluate the efficacy of other weed management practices that can interact with herbicide performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".