Bibliographic record
Abstract
Interest in the discovery and development of plant essential oils for use as bioinsecticides has grown enormously in the past 20 years. However, successful commercialization and utilization of crop protection products based on essential oils has thus far lagged far behind their promise based on this large body of research, most notably because with the exceptions of the USA and Australia, such products receive no special status from regulatory agencies that approve new pesticides for use. Essential oil-based insecticides have now been used in the USA for well over a decade, and more recently have seen use in the European Union (EU), Korea, and about a dozen other countries, with demonstrated efficacy against a wide range of pests and in numerous crop systems. For the most part these products are based on commodity essential oils developed as flavor and fragrance agents for the food and cosmetic industries, as there are formidable logistic, economic, and regulatory challenges to the use of many other essential oils that otherwise possess potentially useful bioactivity against pests. In spite of these limitations, the overall prospects for biopesticides, including those based on essential oils, are encouraging as the demand for sustainably-produced and/or organic food continues to increase worldwide.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".