Ecological Risk Assessments of Metal-containing Substances under Canada’s Chemical Management Plan
Bibliographic record
Abstract
There are approximately 3,000 metal-containing substances on Canada’s Domestic Substances List (DSL). Approximately one third of these substances were identified for further action by the categorization of the DSL, a priority setting exercise completed in 2006 which was based on ecological and human health considerations. Subsequently, the first phase of the Chemicals Management Plan (CMP) was initiated and included activities such as the Challenge initiative to conduct screening assessments on the highest priority chemicals, including a few metal-containing substances. However, to assess the remaining elevated number of metal-containing substances identified as priorities (∼1000) in the next phases of the CMP and that by 2020, a process for finding efficiencies was established. For the risk assessment and risk management of metal-containing substances, efficiencies can be achieved by using a moiety-based approach in which all substances that contain a common metal moiety are assessed simultaneously as a group. This approach also allows for consideration of incidental releases of a given metal. Because ecological concerns were identified during the Challenge for cobalt, an early metal-moiety assessment is being undertaken in the second phase of the CMP for this metal. This work has been initiated and the draft screening assessment is expected to be published in November 2013.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".