Ecologically Based Weed Management
Bibliographic record
Abstract
We define ecologically based weed management to mean a multitactical, integrated, systems approach to managing weeds by the application of complementary, environmentally acceptable weed-control methods. The tactics or methods may include chemical herbicides, tillage, cover crops, crop rotation, competitive cultivars, row spacing, seeding rates, and biological control. To achieve practical, effective, consistent, and economically feasible weed management in crop-production or invasive weed management, the different control tactics should be first and foremost amenable to combinative applications and readily applicable by users. In this chapter, we describe four such tactics involving microorganisms in the broad sense. Included are: (1) biological control by using plant pathogens as bioherbicides, (2) application of deleterious rhizobacteria to suppress weed growth and competition, (3) destruction of soil-borne weed seeds while also controlling plant pathogens, nematodes, and insect pests by creating a managed anaerobic soil disinfestation in preparation for planting crops, and (4) delivering conventional, biochemical (biorational), and genetically designed herbicides encased in biomaterials, including plant virus coat proteins used as nanoparticle carriers. Since these methods utilize microbial agents directly or indirectly for weed management, it is appropriate to consider them to be “ecologically based.” Our objective is to describe and exemplify weed management by these methods and their potential role in the coming decades.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".