Summary of discussions from the 2022 OECD CRP-sponsored conference on innovating microbial pesticide testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it