Summary of Discussions From the 2019 OECD Conference on RNAi Based Pesticides
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
RNA interference (RNAi) is a biological process in which double-stranded ribonucleic acid (dsRNA) molecules inhibit protein expression. In recent years, the application of dsRNA has been used in the development of agricultural products for pest control. The 2019 Organisation for Economic Cooperation and Development (OECD) Conference on RNAi Based Pesticides ("the Conference") brought together academic, industry, and government experts in various aspects of RNAi to discuss the current state of knowledge and topics to help in developing considerations for risk assessment. The Conference focused on environment, with some discussion of human health. Along with presentations on the use of dsRNA-based products in agriculture, government regulation, risk assessment, and a background on the Draft OECD Working Paper on "Considerations for the Environmental Risk Assessment of the Application of Sprayed or Externally Applied dsRNA-Based Pesticides" ("OECD Working Paper"), the Conference included panel discussions from presenters at the end of each session and a larger discussion session with Conference participants on the environmental fate of dsRNA, non-target organism (NTO) risk assessment, and human health risk assessment. This paper summarizes input from presenters and Conference participants during these discussions. Key considerations from these discussions have already been incorporated into the OECD Working Paper, that once finalized and published, will facilitate regulators in evaluating externally applied dsRNA-based products for potential environmental risks.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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