Bibliographic record
Abstract
According to the CAB Direct database of scientific publications, there has been enormous growth in research on botanical insecticides over the past 30 years. In 1980 less than 2% of all journal papers on insecticides dealt with botanicals whereas that proportion exceeded 21% in 2011. In particular there has been explosive growth in studies on insecticidal properties of plant essential oils; over half of the 2,200 papers on essential oils as insecticides have been published since 2006. In contrast, commercialization of botanical insecticides has continued to proceed at a relative snail’s pace, indicating a big disconnect between theory and practice. This is certainly the case in the jurisdictions with the most rigorous regulatory standards – the EU, USA and Japan. Using California as an example, use data for botanical insecticides also suggests a very modest market presence. According to Cal DPR data from 2011, botanicals constituted only 5.6% of all biopesticides used, and less than 0.05% of all pesticide use. However some recently introduced products have seen modest success. On the other hand, there appears to be increasing commercialization of botanical insecticides in China, Latin America and Africa, regions where socio-economic conditions have led to some of the worst examples of human poisonings and environmental contamination. Arguably, botanicals should be of greater value in developing countries where the useful plant species are often locally abundant, accessible and inexpensive. In many tropical countries semi-refined plant preparations are likely to be relatively safe for users and more cost effective than imported conventional crop protection products. In G20 countries botanical insecticides will probably remain niche products for use in public health, urban pest control and in organic food production, but with considerable market opportunities.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".