Policies for Chemical Hazard and Risk Priority Setting: Can Persistence, Bioaccumulation, Toxicity, and Quantity Information Be Combined?
Bibliographic record
Abstract
Existing methods used to screen chemical inventories for hazardous substances that may pose risks to humans and the environment are evaluated with a holistic mass balance modeling approach. The model integrates persistence (P), bioaccumulation (B), toxicity (T), and quantity (Q) information for a specific substance to assess chemical exposure, hazard, and risk. P and B are combined in an exposure assessment factor (EAF), P, B, and T in a hazard assessment factor (HAF), and P, B, T, and Q in a risk assessment factor (RAF) providing single values for transparent comparisons of exposure, hazard, and risk for priority setting. This holistic approach is illustrated using 200 Canadian Domestic Substances List(DSL) chemicals and 12 United Nations listed Persistent Organic Pollutants (POPs). Priority setting results are evaluated with those of multiple category-based screening methods employed by Environment Canada and applied elsewhere that use cutoff criteria in multiple categories (P, B, and T) to identify hazardous chemicals for more comprehensive evaluations. Existing methods have categorized the DSL chemicals as either higher priority (requiring further assessment; screened in) or lower priority (requiring no further action at this time; screened out). The priority setting results of the cutoff-based categorization are largely inconsistent with the proposed integrated method, and reasons for these discrepancies are discussed. Many chemicals screened out using existing methods have equivalent or greater risk potential than chemicals screened in. Decisions for screening assessments using binary classification on the basis of cutoff criteria can be flawed, and complementary holistic methods for priority setting evaluations such as the one proposed should be considered.
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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 | 0.084 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".