General principles for risk assessment of living modified organisms: Lessons from chemical risk assessment
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
Modern biotechnology has led to the development and use of Living Modified Organisms (LMOs) for agriculture and other purposes. Regulators at the national level are increasingly depending on risk assessment as a tool for assessing potential adverse effects of LMOs on the environment and human health. In addition, the Cartagena Protocol on Biosafety, an international agreement expected to enter into force in the near future, requires risk assessment as the basis for decision-making regarding import of some LMOs. While LMO risk assessment is relatively new, there are other risk assessment disciplines which have developed over longer time periods. The field of assessment of the environmental and human health risks of chemicals is particularly well developed, and is similar in application to LMO risk assessment. This paper aims to draw lessons for LMO risk assessment from the vast experience with chemical risk assessment. Seven general principles are outlined which should serve as a useful checklist to guide assessments of risks posed by LMOs.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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