Monitoring Sublethal Toxicity in Effluent Under the Metal Mining EEM Program
Bibliographic record
Abstract
Abstract The second national application of environmental effects monitoring (EEM) in Canada will be under the amended Metal Mining Effluent Regulations (MMER). Under the EEM program, sublethal toxicity testing will be included in a suite of complementary tools to assess whether fish populations, fish habitat, and use of the fisheries resource, are protected in water bodies receiving mining effluent. The rationale for including sublethal toxicity tests was provided by an extensive literature review during the Aquatic Effects Technology Evaluation (AETE) program that showed 84% agreement between sublethal toxicity test results and observed impacts on receiving water. In addition, laboratory testing during the AETE program identified tests which would be the most cost effective and efficient in assessing mining effluent toxicity. Based on the findings of the AETE program, the multistakeholder EEM Metal Mining Working Group and its Toxicology Subgroup selected tests on fish, invertebrates, algae and an aquatic plant species, and developed consensus recommendations on the minimum requirements for sublethal toxicity testing and technical guidance on how to implement the recommended monitoring. The rationale for method selection and application of test results, and general testing requirements are discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".