Integrated pest management and weed management in the United States and Canada
Bibliographic record
Abstract
There is interest in more diverse weed management tactics because of evolved herbicide resistance in important weeds in many US and Canadian crop systems. While herbicide resistance in weeds is not new, the issue has become critical because of the adoption of simple, convenient and inexpensive crop systems based on genetically engineered glyphosate-tolerant crop cultivars. Importantly, genetic engineering has not been a factor in rice and wheat, two globally important food crops. There are many tactics that help to mitigate herbicide resistance in weeds and should be widely adopted. Evolved herbicide resistance in key weeds has influenced a limited number of growers to include a more diverse suite of tactics to supplement existing herbicidal tactics. Most growers still emphasize herbicides, often to the exclusion of alternative tactics. Application of integrated pest management for weeds is better characterized as integrated weed management, and more typically integrated herbicide management. However, adoption of diverse weed management tactics is limited. Modifying herbicide use will not solve herbicide resistance in weeds, and the relief provided by different herbicide use practices is generally short-lived at best. More diversity of tactics for weed management must be incorporated in crop systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".