Bibliographic record
Abstract
This review focuses on proactive and reactive management of glyphosate-resistant (GR) weeds. Glyphosate resistance in weeds has evolved under recurrent glyphosate usage, with little or no diversity in weed management practices. The main herbicide strategy for proactively or reactively managing GR weeds is to supplement glyphosate with herbicides of alternative modes of action and with soil-residual activity. These herbicides can be applied in sequences or mixtures. Proactive or reactive GR weed management can be aided by crop cultivars with alternative single or stacked herbicide-resistance traits, which will become increasingly available to growers in the future. Many growers with GR weeds continue to use glyphosate because of its economical broad-spectrum weed control. Government farm policies, pesticide regulatory policies and industry actions should encourage growers to adopt a more proactive approach to GR weed management by providing the best information and training on management practices, information on the benefits of proactive management and voluntary incentives, as appropriate. Results from recent surveys in the United States indicate that such a change in grower attitudes may be occurring because of enhanced awareness of the benefits of proactive management and the relative cost of the reactive management of GR 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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".