Summary of discussions from the 2022 OECD CRP-sponsored conference on innovating microbial pesticide testing
Bibliographic record
Abstract
Microbial pesticides are a class of biopesticide that includes microorganisms, such as bacteria, fungi, viruses, and protozoa, that are applied for pest control. Mammalian (human health) and non-target organism hazard testing are required to support registrations of microbial pesticides; however, developers and regulators of microbial pesticides face both new and old challenges for testing. New challenges include how to design or adapt new approach methodologies (NAMs), typically developed for chemicals, to mammalian health testing for microbial pesticides. Older challenges involve need for improved guidance for hazard testing with non-target organisms. Both are viewed as potential barriers to the development and adoption of microbial pesticides, which are potential alternatives to chemical pesticides. The 2022 conference, Innovating Microbial Pesticide Testing (hereafter, “the Conference”), sponsored by the Organisation for Economic Cooperation and Development (OECD) Cooperative Research Program (CRP), brought together experts on these topics from academic, industry, government, and non-governmental organizations to discuss the above challenges and establish plans to address them. Speakers presented on their perspectives of the challenges and potential solutions, which informed and guided panel discussions. This paper summarizes the contributions from presentations and panel discussions toward the conference conclusions and resulting workplans.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".