Sublethal Toxicity Testing of Canadian Metal Mining Effluents: National Trends and Site-Specific Uses
Bibliographic record
Abstract
As part of the Canadian Environmental Effects Monitoring program under the National Metal Mining Effluent Regulation, there is a requirement to conduct sublethal toxicity tests twice per year for the first three years. These first three years (2003 to 2005) were considered a period of initial monitoring and resulted in test endpoints for each of the required standardized methods on a fish, an aquatic plant, an invertebrate, and an algal species. On a national level (based on 1648 valid results), the test from most to least sensitive was: the inhibition of reproduction with Ceriodaphnia dubia, the growth inhibition (frond number) with Lemna minor, the inhibition of cell yield with Pseudokirchneriella subcapitata, the growth inhibition (dry weight) with Lemna minor, the growth inhibition of fathead minnows, and the effect on embryo viability with rainbow trout. This sensitivity ranking changed when tests were further evaluated on a geographical region and mine-type basis (e.g., base metal, precious metal, uranium, iron ore). Site-specific examples show how sublethal toxicity data are being used to track changes in effluent quality, choosing a final discharge point, monitoring multiple discharges to the same watercourse, and to identify study design weaknesses by comparing laboratory results to field survey conclusions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".