A good practice guide for safe work with nanoparticles: The Quebec approach
Bibliographic record
Abstract
new industrial revolution has already begun around nanotechnologies, letting us anticipate major scientific breakthroughs that will affect each economic activity sector and whose expected global economic impacts will exceed $1000 billion annually by 2012. Simultaneously, many studies reveal that nanoparticles represent different occupational health and safety (OHS) risks unique to them and that often differ from the risks related to the same chemical substances with larger dimensions. As the number of potentially exposed workers increases and much uncertainty persists about OHS risks, this extended abstract proposes a framework for occupational risk management with the objective of controlling exposure to NPs in a context of a major lack of specific data related to the hazards of these substances and to the level of occupational exposure. The framework takes into consideration the equal representation of both the employers and workers in the Québec legislation and accounts the potential routes of exposure and focuses on a structured approach dealing with hazard identification, exposure characterization, risk assessment and risk management through different control methodologies. These are included in a prevention program that must be followed up, once it has been implemented, and refined through an iterative approach as new data become available.
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.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.024 |
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".