Evaluation of Lethal Fish Sampling and the Nonlethal Alternative Under the Canadian Metal and Diamond Mining Environmental Effects Monitoring Program
Bibliographic record
Abstract
The Environmental Effects Monitoring (EEM) program, federally mandated to metal and diamond mines and mills in Canada, aims to ensure that fish, fish use, and their environment are sufficiently protected by the Metal and Diamond Mining Effluent Regulations (MDMER). Concerns have been raised regarding lethal fish sampling conducted for the program and the potential risk to fish populations. This study endeavored to calculate the extent of fish sacrificed during lethal EEM fish surveys to provide a more informed characterization for stakeholders. By extrapolating data from 28 studies conducted at 8 metal mines in Saskatchewan between 2004 and 2017, it was conservatively estimated that 60 060 ± 19 978 fish have been sacrificed for the program nationwide since its promulgation in 2002. This is almost 2 times greater than guidance recommendations due to numerous factors. Challenges faced when implementing the nonlethal fish population survey alternative were also evaluated, and some of these included data interpretation inconsistencies due to the lack of critical effect sizes (CES) included in the regulations for nonlethal effect endpoints, and reliance on capturing young of the year fish to assess the prescribed effect endpoints. Finally, a case study conducted to evaluate the congruity of lethal and nonlethal fish population survey results identified that the effect endpoints often produced conflicting conclusions for the same response variable. Ultimately, this study highlights that in order to strengthen and promote the use of nonlethal fish sampling as an alternate to the current standard of conducting lethal fish population surveys under the EEM program, the effect endpoints, study designs, and adoption of CES need to be thoroughly evaluated and included in the MDMER. If nonlethal sampling designs adequately provide the required data and meet program objectives, then there would be no need to continue sacrificing fish for the Canadian metal and diamond mining EEM program. Integr Environ Assess Manag 2019;00:1-10. © 2019 SETAC.
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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 | 0.044 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".