Consumer Exposure Scenarios in the Health Canada Existing Substances Program
Bibliographic record
Abstract
TS1-13 Abstract: Canada is the first country to introduce a legislative requirement for systematic priority setting for all existing chemicals. In addition to a continuing mandate to establish and conduct full assessments for lists of priority substances, the Canadian Environmental Protection Act (CEPA '99) requires that the Ministers of Health and Environment complete “categorization” (priority setting) of all of the approximately 23,000 substances on the Domestic Substances List (DSL) by September 2006, with subsequent screening and full risk assessment, when warranted. These requirements set the stage for identification of highest priority substances for subsequent introduction of control measures to reduce exposure in both consumer products and the general environment. This precedent setting mandate has required the development and refinement of methodology for priority setting and risk assessment for a wide range of diverse substances. These approaches draw maximally from available, often generic information as a basis to consider large numbers of substances, for which individual data on exposure and hazard are often limited. Estimation of exposure to consumer products is addressed both in priority setting and assessment stages of the program. For consumer products, an approach has been developed to provide quantitative estimates of exposure relevant in a priority setting context. This has required consideration of the relative degree of conservatism in existing exposure modeling algorithms and development of a considerable number of additional scenarios and leads to quantitative plausible maximum estimates of exposure of individuals in the general population by age group based on use scenario, physical/chemical properties, and bioavailability. Comparison of the output with measures of exposure–response for relevant critical effects leads to substances being set aside from further consideration or prioritized for additional assessment. After 2006, the approach will also contribute to efficient screening, delineating the focus of subsequent assessment. The development and integration of consumer exposure modeling in increasingly broad legislative mandates to systematically consider all existing chemicals raises a number of issues relevant to exposure assessment in differing jurisdictions. These include transparency, consistency, usability, and defensibility of the models, including relevant degree of complexity for priority setting versus assessment. These issues are discussed through examples and lessons learned from the development of the approach to consumer products for priority setting and screening assessment.
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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 | 0.001 | 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".