Substance Profiling for Categorization and Screening Health Risk Assessments of Existing Substances Under the Canadian Environmental Protection Act (CEPA)
Bibliographic record
Abstract
TS1-17 Abstract: The Canadian Environmental Protection Act (CEPA) requires that the Ministers of Health and the Environment complete “categorization” (prioritize) of the approximately 23,000 substances on the Domestic Substances List (DSL) by September 2006 for subsequent screening and/or full risk assessment. Health Canada is to identify those substances that pose the greatest potential for exposure to the general population and those that are “inherently toxic” to humans for a subset identified as persistent and/or bioaccumulative by Environment Canada. A multitiered approach, including novel simple and complex tools, is applied to efficiently identify those substances of highest priority for further assessment based on relative risk to human health in Canada. Application of the tools and approaches particularly requires for large numbers of substances collection and recordkeeping for significant amounts of information to transparently delineate the basis for decision-making. It has also led to development of robust search strategies for both use and hazard profiling to maximize identification of relevant data often from obscure sources for the significant numbers of compounds being considered. Experience on the program in data-accessibility relevant to use and exposure profiling, management, and the availability of output of categorization and implications for assessment priority are described.
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 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 | 0.002 | 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 | 0.001 | 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".