Practicalities of developing and registering microbial biologicalcontrol agents.
Bibliographic record
Abstract
Abstract There is considerable interest in the exploitation of microbial biological control agents (MBCAs) for the control of crop pests, weeds and diseases. MBCAs can be used where chemical pesticides are banned or being phased out or where pests have developed resistance to standard chemicals. The use of MBCAs can play an important role in crop protection, as a key element in integrated pest management (IPM) programmes. However, despite considerable research efforts on the development of new biological control agents the number of such products on the market in the European Union (EU) is still extremely low compared with the USA or Canada. In areas that previously constrained the commercialization of MBCAs, discovery, fermentation, formulation and application, significant progress has been made. The low number of products is mainly due to the slow registration process. In the EU, MBCAs are regulated by and follow Directive 91/414/EEC for placing plant protection products in the market. Once an active ingredient is listed in Annex I, national registrations for the formulated product have to follow. This time consuming and expensive process has forced most companies to suspend their efforts in research and development. Initiatives by stakeholders from industry, science, regulatory authorities, policy and environment are underway to accelerate market introduction of MBCAs.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".