Recent Developments in the Registration and Usage of Botanical Pesticides in California
Bibliographic record
Abstract
California Department of Pesticide Regulation’s Pesticide Use Report (PUR) and Pesticide Label databases provide a unique opportunity to analyze trends in the plant-derived (botanical) pesticide industry in California based on product registration and commercial usage. While the role of botanical pesticides in commercial pest control remain minor, the number of registered botanical pesticide products (indicating registrants’ confidence in their marketability) and the mass of botanicals used commercially have seen marked increases in recent years. Registered product counts (as total number of products by 7/7/2017) are high and usage remains steady for the classical botanicals pyrethrins (4,862 products, ~250,000 kg products/yr) and strychnine (617 products, ~50,000 kg products/yr). By 2015, registration and usage had grown for some newer botanicals, including azadirachtin (65 products, ~150,000 kg/yr) and limonene (145 products, ~40,000 kg/yr). Pesticidal activity of essential oils often require multiple components, and the 37 different essential oils registered for use in California have achieved only marginal implementation according to PUR usage. As in the pyrethrin/pyrethroid story that began in the 1960s, natural products continue to serve as leads for new high-usage synthetics. For example, a unique intracellular calcium receptor class, discovered using the natural alkaloid ryanodine, has been used to develop the synthetic ryanoid insecticides chlorantraniliprole, cyantraniliprole and flubendiamide. Entering the California market in 2009, ryanoids achieved ~350,000 kg of product usage in 2015. These trends underscore the importance of continuing the search for plant natural products with potent insecticidal activities, as they may lead to powerful synthetics with new modes of action in order to mitigate resistance to existing high-use pesticides.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".