Familiarity in the Context of Risk Assessment of Transgenic Crops: Focus on Some Countries in the Americas
Bibliographic record
Abstract
Problem formulation is the formal opening stage of a risk assessment that determines its purpose and scope and hence guides the gathering of information data. The concepts of familiarity and history of safe use are an integral part of problem formulation. These concepts do not replace the case-by-case approach and are not taken as safety standards but are valuable components of the process that shape the generation of plausible, testable risk hypotheses. The International Life Sciences Institutes in Brazil and Argentina have facilitated numerous discussions on the scientific principles for risk assessment of transgenic crops in the Latin American region in the past 5-6 years. The session held at ISBR 15th elaborated on the familiarity concept and derived tools and their role in the evolution of risk evaluation criteria. Examples of how different countries in the Americas interpret and apply these conceptual tools show that familiarity is a valuable concept, although terms are very often confused and vaguely defined. Formalizing these terms with clear definitions and scope of application in guidelines and regulatory documents would reduce ambiguity, enhance predictability, and add transparency to the evaluation processes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".