Human Risk Assessment and Its Application to Nanotechnology: A Challenge for Assessors
Bibliographic record
Abstract
Scientific literature suggests that exposure to nanoparticles (NPs) might be associated with adverse health effects. A well-developed human risk assessment (HRA) that applies to NPs has never been established and optimized–until now. Furthermore, no government regulations are in place that establish what is considered to be an adequate and secure level of exposure and supported by a strong scientific approach for nanotechnology. It is important to implement the HRA to ensure that workers producing NPs, users of NPs and the general population are protected from deleterious issues related to NPs. In this work, a methodology is described based on the HRA. An effort is required during synthesis before the commercialization phase to evaluate the results of a systematic and rigorous assessment because this could significantly reduce the health risks of those exposed to NPs, including workers and the population.
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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 | 0.035 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".