Moving Towards a Safe(r) Innovation Approach (SIA) for More Sustainable Nanomaterials and Nano-enabled Products
Bibliographic record
Abstract
This report aims to contribute to the discussion on a ‘Safe(r) Innovation Approach’ for more sustainable nanomaterials and nano-enabled products. The document presents common working descriptions to ensure a common understanding of concepts such as Safe(r) Innovation Approach and its elements, Safe(r)-by-Design and Regulatory Preparedness. The document compiles existing risk assessment tools, frameworks and initiatives developed for Safe(r)-by-Design. The inventory of risk assessment tools and frameworks should contribute to assisting industry in implementing a 'Safe(r) Innovation Approach' for NMs and nano-enabled products. This includes a review of lessons learned from applying existing Safe(r)-by-Design concepts and tools and methods applied in hazard, exposure and risk assessment and management along the innovation value chain. Additionally, it assesses the applicability of Safe(r)-by-Design through case studies and existing initiatives, analysing constraints and limitations. The report also compiles information on regulatory initiatives related to the review of innovative approaches and technologies. Finally, it gathers information on regulatory initiatives related to innovative approaches and assesses their integration into current legislation or guidance.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".