Saúde & Segurança Ocupacional: reflexões sobre os riscos potenciais e o manuseio seguro dos nanomateriais
Bibliographic record
Abstract
Every day the nanotechnology, that refers to a field whose theme is the control of matter on an atomic and molecular scale working with nanometric structures (<100 nm), is more present in the development of products and industrial processes. The particle manipulation of nanometric structures has created opportunities in the development of new products and materials. However, synthesis, handling, storage, stabilization and the incorporation of these materials, with nanometric dimensions, demand a new perspective of analysis and evaluation of old manufacturing processes, procedures and industrial devices, in order to guarantee collective and individual protection to workers and society. With the increasing of scale and production of nanoestrutuctured materials, a big part of labour community starts to be in contact with different nanomaterials (forms and ways). In this work the main aspects and involved risks of manufacture, storage, synthesis, stabilization and incorporation of nanomaterials on new products are evaluated in order to reduce, decrease and eliminate chemical, physical and biological risks for the employees. A bibliographic review was conducted about risk, safety and nanotechnology based on available English literature focusing safety and environmental agencies from different countries such as USA, Canada, EU (France, UK, Germany, Denmark), Australia and Japan.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".