Bibliographic record
Abstract
This review is both a retrospective (what have we missed?) and prospective (where are we going?) examination of weed control and technology, particularly as it applies to herbicide-resistant weed management (RWM). Major obstacles to RWM are discussed, including lack of diversity in weed management, unwillingness of many weed researchers to conduct real integrated weed management research or growers to accept recommendations, influence or role of agrichemical marketing and governmental policy and lack of multidisciplinary research. We then look ahead to new technologies that are needed for future weed control in general and RWM in particular, in areas such as non-chemical and chemical weed management, novel herbicides, site-specific weed management, drones for monitoring large areas, wider application of 'omics' and simulation model development. Finally, we discuss implementation strategies for integrated weed management to achieve RWM, development of RWM for developing countries, a new classification of herbicides based on mode of metabolism to facilitate greater stewardship and greater global exchange of information to focus efforts on areas that maximize progress in weed control and RWM. There is little doubt that new or emerging technologies will provide novel tools for RMW in the future, but will they arrive in time?
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".