New technologies could enhance natural biological control and disease management and reduce reliance on synthetic pesticides
Bibliographic record
Abstract
A shift to larger farms, bigger equipment and reduced crop diversity has been occurring in North American agriculture for many years. This has resulted in an increased reliance on genetic resistance and pesticides because much of the pest reduction from natural biological control (ecosystem services) associated with crop rotation and biological diversity has been lost. This shift has contributed to erosion of cultivar resistance and loss of sensitivity to pesticides. However, the impending change to autonomous field equipment represents an opportunity to reverse the trend towards ever-larger farm equipment. Small autonomous units have the potential to make intercropping, deployment of multi-lines, precision agriculture and even crop rotation, easier and more cost-effective. Remote sensing using drones, combined with precision application of pesticides and improved weather forecasts to help select optimum conditions, could improve the efficacy of both biocontrol agents and synthetic pesticides. Similarly, technologies such as marker-assisted selection for complex traits, gene editing to provide novel sources of resistance, and RNAi (gene silencing) to manage target pest populations could reduce reliance on synthetic pesticides. Crop rotation and improved strategies for deploying genetic resistance, combined with smaller fields, improved scouting and optimized pesticide application, could shift the balance back towards biological diversity and natural biological control within fields. Adding improved genetics for resistance to this mix could further reduce the need for large-scale pesticide application, and minimize both the use and impact of synthetic pesticides in agricultural systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".