A Review of Recent Patents on Macroorganisms as Biological Control Agents
Bibliographic record
Abstract
The indiscriminate use of synthetic pesticides has brought undesired problems to human health, agriculture, and the environment. Integrated Pest Management (IPM) and Biological Control (BC) programs, which are based on minimum use of pesticides, are seen as alternative, more ecological solutions to the unintended problems associated with pesticide use. These programs combine the introduction, augmentation, and/or conservation of pest natural enemies, with other protection tools. Although patents and the process of commercialization of microorganisms has been the subject of various reviews, macroorganisms used for pest and disease control have stimulated less comprehensive analyses. From our review of patents, there has been an enormous increase in the number of macroorganism-related patents registered in the last two decades. Private companies own 65% of all these patents. Rearing methods and crop protection strategies are the main intellectual property patented, with parasitoid wasps and predatory mites being the primary Biological Control Agent (BCA) focus of patents. Among countries, Japan was the first country with these types of patents, followed by the United States, Canada and China. Increasing concern for pesticide risks by governments and the public is seen as the main impetus for change in "traditional" crop protection practices and for investment in other more ecological products like BCAs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".