A Web Tool for the Identification of Potential Interactive Effects of Chemical Mixtures
Bibliographic record
Abstract
This project was undertaken to develop a toxicological database allowing the identification of possible additive or other interactive effects of mixtures present in the work environment. In the first phase of the project, whose findings have already been published, critical toxicological data were compiled for each of the 695 chemical substances in the Quebec Occupational Health Regulation, allowing the prediction of potential additivity among components of a mixture. In the second phase of this project, the types of interactions for mixtures most likely to be found in workplaces and for which primary literature data are available were specified. The toxicological data were evaluated only for realistic exposure concentrations up to the short-term exposure limit or ceiling value or five times the 8-hr time-weighted average (TWA) permissible exposure limit (PEL) for human data and up to 100 times the 8-hr TWA PEL or ceiling value for animal studies. In total, 675 studies were evaluated covering 209 binary mixtures of substances. For the majority of cases where potential additivity was identified in Phase 1, there is a lack of toxicological data in the primary literature. In these cases, the results of the first phase will be useful as the default hypothesis. The resulting database integrates the results from both phases of the project. A web-based computer tool allows the user to determine whether there is potential additivity or interaction among components of a mixture.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 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".