CALL FOR THE DEVELOPMENT OF AN ADAPTATIVE TOOL FOR ASSESSING HUMAN HEALTH POSED BY
Bibliographic record
Abstract
3Institut de recherche Robert-Sauvé en santé et en sécurité du travail, Canada. Assessing the risks associated with engineered nanoparticles (particles having at least one dimension in the 1–100 nm range) faces three major challenges: (1) lack of standard methodological approaches; (2) uncertainty surrounding the risk factors and their relative signifi cance; and (3) lack of control strategies. Among the approaches that have been proposed are (1) adapting risk evaluation tools used in industrial hygiene; (2) use of evaluation concepts borrowed from the insurance industry; (3) determining the consensus among experts; (4) rating risk control measures; (5) construction of infl uence diagrams; and (6) use of techniques drawn from multi-criteria decision-making. Knowledge has advanced rapidly in the fi eld of engineered nanoparticles, but comparison of studies is diffi cult and major gaps remain in the characterization of these materials and the risks they represent. Since they are already being introduced into commercial products and processes, the need is urgent for a fl exible and dynamic tool for compiling and sharing detailed knowledge of the associated risks. Uncertainties need to be expressed and reduced. This tool must aid the decision-making of business managers, scientists, and other stakeholders. To the best of our knowledge, no approach suggested in the literature meets these criteria. Thus, the authors call to develop an adaptive, multidimensional decision support tool that indicates infl uence relationships among risk factors and fosters the gathering and sharing of knowledge, including uncertainties.
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 | 0.038 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".