Prioritization and assessment of flame retardants under Canada’s Chemicals Management Plan: Looking back and priorities moving forward
Bibliographic record
Abstract
Presented by: Peter Mochungong – Scientific Evaluator at Health Canada, peter.mochungong@canada.ca \n \nCo-authors: Leona MacKinnon, Angelika Zidek \n \nAbstract: Canada’s Chemicals Management Plan (CMP) is an initiative aimed at reducing the risk posed by chemicals to Canadians and their environment. Under the CMP, the Government of Canada (GoC) is responsible for prioritizing and assessing those substances on the Domestic Substances List (i.e., substances in commerce in Canada between 1984-1986) under the purview of the Canadian Environmental Protection Act (CEPA), 1999. A number of flame-retardants were identified as priorities in 2006 and have since been assessed including tributyl phosphate (TBP), tetrabromobisphenol A (TBBPA), dechlorane plus (DP), decabromo diphenyl ethane (DBDPE), hexabromocyclo dodecane (HBCD) and tri(chloroethyl) phosphate (TCEP). Those with identified human health or ecological concerns are subsequently risk managed. Recognizing that new information continues to be generated that could help inform the identification of substances of concern; a regular review of available information is undertaken by the GoC. The approach, known as the Identification of Risk Assessment Priorities, enables the GoC to communicate how emerging issues are tracked, and to identify and prioritize substances requiring further work. Through this approach, 36 substances that are structurally related to organic flame-retardants previously assessed under the CMP or potentially used as flame-retardants have been recommended for further scoping. \n \nBiography: Dr. Peter Mochungong holds a PhD in Environmental Health Sciences from the University of Southern Denmark. He first joined Health Canada 9 years ago as NSERC Visiting Fellow to Government of Canada Laboratories; conducting environmental contaminants research, especially the presence and fate of volatile and semi-volatile organic contaminants in air and consumer products. Dr. Mochungong is passionate about research, developing new methodologies and interpretation of toxicology and exposure data on chemical risk assessment to inform evidence-based policies and regulations. Dr. Mochungong currently works as Scientific Evaluator at Health Canada.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".