Weed Dynamics and Management Strategies for Cropping Systems in the Northern Great Plains
Bibliographic record
Abstract
Cropping systems in the northern Great Plains (NGP) have evolved from wheat (Triticum aestivum L.)–fallow rotations to diversified cropping sequences. Diversification and continuous cropping have largely been a consequence of soil moisture saved through the adoption of conservation tillage. Consequently, weed communities have changed and, in some cases, become resistant to commonly used herbicides, thus increasing the complexity of managing weeds. The sustainability of diverse reduced tillage systems in the NGP depends on the development of economical and effective weed management systems. Utilizing the principle of varying selection pressure to keep weed communities off balance has reduced weed densities, minimized crop yield losses, and inhibited adverse community changes toward difficult‐to‐control species. Varied selection pressure was best achieved with a diverse cropping system where crop seeding date, perennation, and species and herbicide mode of action and use pattern were inherently varied. Novel approaches to cropping systems, including balancing rotations between cereal and broadleaf crops, reducing herbicide inputs, organic production, fall‐seeded dormant canola (Brassica napus and B. rapa), and the use of cover crops and perennial forages, are discussed in light of potential systems‐level benefits for weed management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".