Persistence Strategies of Weeds
Bibliographic record
Abstract
Undesirable plants continue their presence in agricultural and managed ecosystems despite a wide variety of management techniques developed to eliminate them. We definerefer to weed persistence as “the ability of undesirable plant species to continually evolve, survive, thrive, and reproduce under a variety of natural and anthropogenic selection pressures.” The study of persistence strategies of weeds is fundamental to the development of integrated weed management strategies. Therefore, it is important to ask the following questions: (i) What makes a weed so persistent? (ii) Why are the natural and human-developed selection pressures failing to eliminate them? (iii) What lessons can we learn from the strategies that theyhave developed to persist in the various ecosystems? (iv) How can we use our understanding of persistence strategies of weeds to minimize the damage they cause to agroecosystems and other human-managed ecosystems? In this chapter we discuss how prolific seed production, efficient dissemination, maintenance of soil seedbanks, and in many cases perennial growth of weeds facilitate their persistence. We also discuss the importance of seed dormancy in persistence. We emphasize the need for more research on the roles of climate change, microbial communities, seed predation, agronomic practices, and allelochemicals in influencing weed persistence. Since at the root of weed persistence is the ability of weeds to evolve in response to various selection pressures, we advocate that understanding this ability is the way forward to develop better management options in this age-old battle between humanity and the weeds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".