Artificial Streams for Environmental Effects Monitoring (EEM): Development and Application in Canada over the Past Decade
Bibliographic record
Abstract
Abstract Development of artificial stream systems has been an on-going research effort in Canada over the past decade. At the National Water Research Institute (NWRI) of Environment Canada, artificial stream systems have been developed to assess the effects of point source effluents on aquatic biota. Initial applications (1990–1994) focused on assessing the effects of pulp mill effluents on benthic invertebrate and algae communities in large western Canadian rivers. Artificial streams were then used to assess the effects of pulp mill effluents on fish in marine and estuarine environments in eastern Canada (1997–1999). Most recently (2000–2001) artificial stream systems have been developed as tools to evaluate the effects of mining effluents on fish and benthic invertebrates. In addition, multi-trophic level (algae + benthic invertebrate + fish) applications have been developed for cumulative effects bioassessment. Based upon this culmination of research and development, artificial stream systems have been incorporated into the federally legislated Environmental Effects Monitoring (EEM) program as an alternative to field surveys for assessment of pulp and paper and mining pollution. The Canadian experience in development of artificial stream systems should serve as a model to demonstrate how research tools can be incorporated into federally legislated monitoring programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".