Herbicide-Resistant Weeds: Management Tactics and Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In input-intensive cropping systems around the world, farmers rarely proactively manage weeds to prevent or delay the selection for herbicide resistance. Farmers usually increase the adoption of integrated weed management practices only after herbicide resistance has evolved, although herbicides continue to be the dominant method of weed control. Intergroup herbicide resistance in various weed species has been the main impetus for changes in management practices and adoption of cropping systems that reduce selection for resistance. The effectiveness and adoption of herbicide and nonherbicide tactics and practices for the proactive and reactive management of herbicide-resistant (HR) weeds are reviewed. Herbicide tactics include sequences and rotations, mixtures, application rates, site-specific application, and use of HR crops. Nonherbicide weed-management practices or nonselective herbicides applied preplant or in crop, integrated with less-frequent selective herbicide use in diversified cropping systems, have mitigated the evolution, spread, and economic impact of HR weeds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it