Key Exposure Models and Input Parameters Used for Assessing Exposure to Consumer Products Under Canada's Chemicals Management Plan
Bibliographic record
Abstract
Exposure models and associated input parameters are frequently used to estimate human exposure to chemicals in products available to consumers. The generation of intake estimates based on these models is often critical to assessing risks as mandated under Canada’s Chemicals Management Plan (CMP). This poster presentation will highlight the impacts and challenges associated with using various exposure models based on the analysis of several published assessments conducted over the last decade. This includes a discussion on the rationale for the use of the models, and our tiered approach for refinements to exposure scenarios. The effects on the exposure estimates from modifying key parameters (e.g. mass transfer rate, product amount, and room volume) will be explored along with the presentation of case studies. Key similarities and differences between various consumer exposure models, including RIVM’s ConsExpo, US EPA’s Consumer Exposure Model (CEM), and Residential SOPs will be highlighted. This poster presentation showcases the decision making process when considering the models to use for characterizing exposure in assessments conducted under the CMP and exploratory work on new tools that can be used in future risk assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".