1150 Mixie, a tool to improve assessment of chemical risk in case of multiple exposure
Bibliographic record
Abstract
Introduction Multiple exposure to chemicals is a common situation in workplaces. However, most methods used to evaluate chemical risk do not consider the potential effects of mixtures. The aim is to present a tool helping to evaluate chemical risk in case of multiple exposure. Methods MiXie is a web tool (http://www.inrs-mixie.fr/http://www.inrs-mixie.fr/) developed in Quebec in 1997 and adapted to the French context by the French National Research and Safety Institute for the prevention of occupational accidents and diseases (INRS). It helps industrial hygienists to assess the potential risk of multi-exposure. Additivity of effects is the basic assumption. Results Whenever measurements of atmospheric concentrations are provided, MiXie calculates the exposure index of the mixture (i.e. the sum of the ratios between each concentration measured and its occupational exposure limit value x 100). If this index exceeds 100%, MiXie signals that there is a potential risk for certain organs, even though each limit value is respected. When measurements of atmospheric concentrations are not provided, MiXie highlights the common effects classes of the substances present and warns about a potential risk of additive effects. If the mixture contains a substance belonging to the ‘cancer’ or ‘sensitizer’ effect class, additivity does not apply and MiXie warns the industrial hygienist regardless of the concentration measured. Conclusion The MiXie database helps to identify potential risk situations related to multi-exposure to chemicals. Such situations may go unnoticed with a monosubstance approach. But MiXie users should be aware of its limits: additivity does not apply to all situations, the number of substances is restricted (130), etc. Experts are working to improve the tool by increasing the number of substances, making it more user-friendly, etc.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.020 |
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".