Development of Botanical Pesticides for Public Health
Bibliographic record
Abstract
Abstract Pesticide science is faced with a growing demand for green or sustainable pesticide chemistries that offer reduced risks to human health and the environment. Efforts are placed on the development of new pesticides containing novel active ingredients and/or formulations from natural sources. These products continue to drive innovation and have been proven to present effective alternatives to conventional pesticides. The use of botanical extracts for management of arthropods can be traced back through centuries, and their biological properties continue to be explored in the scientific literature. Many of the terpenoid compounds contained in plant essential oil extracts are capable of eliciting strong inhibitory effects against arthropods in laboratory settings; however, effective delivery can pose many challenges in the formulation process including selection of active ingredients, emulsifiers, spreaders, and other necessary components. Recent data demonstrates that select botanical terpenes/plant essential oils can control public health pests under field and laboratory settings, but current products may not necessarily encompass the full potential of their active ingredients. Further research continues to improve on botanical formulations and offer new approaches, such as microencapsulation, for use in product development. These findings highlight improvements in the formulation of plant essential oil active ingredients, and provide support for using botanicals to control public health pests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".