Developing an Integrated Weed Management System for Herbicide-Resistant Weeds Using Lentil (<i>Lens culinaris</i>) as a Model Crop
Bibliographic record
Abstract
The escalating evolution of weed species resistant to acetolactase synthase (ALS)-inhibitor herbicides makes alternative weed control strategies necessary for field crops that are dependent on this herbicide group. A fully integrated strategy that combined increased crop seeding rates (2X or 4X recommended), mechanical weed control with a minimum-tillage rotary hoe, and reduced-rate non–ALS inhibitor herbicides was compared with herbicides, rotary hoe, and seeding rates alone as a method of controlling ALS inhibitor–tolerant Indian mustard as a model weed. The full-rate herbicide treatment had the lowest weed biomass (98% reduction) and the highest yield of all treatments in 3 of 4 site-years, regardless of seeding rate. The fully integrated treatment at the 4X seeding rate had weed suppression rates equal to the full herbicide treatment at the recommended seeding rate. The fully integrated and reduced-rate herbicide treatments at the 4X seeding rate reduced weed biomass by 89% and 83%, respectively, compared with the control at the recommended seeding rate. The rotary hoe treatment alone resulted in poor weed control (≤38%), even at the highest seeding rate. Fully integrated and reduced-rate herbicide treatments at 2X and 4X seeding rates had yields equal to those of the full herbicide treatment at the recommended seeding rate. Partially or fully integrated weed control strategies that combine increased crop seeding rates and reduced-rate non–ALS inhibitor herbicides, with or without the use of a rotary hoe, can control weeds resistant to ALS-inhibitor herbicides, while maintaining crop yields similar to those achieved with full-rate herbicides. However, combining increased seeding rate, reduced-rate herbicides, and mechanical rotary hoe treatment into a fully integrated strategy maximized weed control, while reducing reliance on and selection pressure against any single weed control tactic.
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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 | 0.001 | 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 | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".