Integrating mesocosm experiments with field and laboratory studies to generate Weight-of-Evidence risk assessments for ecosystem health
Bibliographic record
Abstract
Assessing ecosystem health of large rivers is often complicated because these ecosystems receive multiple, interacting effluent discharges. Confounding factors, such as complicated mixing hydraulics and historical loading effects, can result in equivocal field data that lend weak inference to ecological risk assessments. Within Environment Canada's National Water Research Institute (NWRI) one approach to this problem develops a strategy that defines important mechanisms of pollutant effects through the combined use of laboratory and field measurements, riverside mesocosm experiments, and the incorporation of indicators at several trophic levels. We integrate these different types of information through weight-of-evidence postulates that provide logical guidelines for establishing causation in ecological risk assessment. Using this approach, retrospective risk assessments on a number of Canadian rivers indicates that the major effect of present effluent discharges from pulp mills has been one of nutrient enrichment and stimulation of food web productivity. The combination of field experiments with this weight-of-evidence approach yielded the scientific justification for a conceptual model that describes community shifts across a nutrient-contaminant gradient. The purpose of this is paper is to illustrate the utility of riverside mesocosms in assessing ecological impacts of point-source discharges. We conclude that the use of stream mesocosms with the weight-of-evidence approach is highly useful for establishing a mechanistic understanding of community responses to stressors at a regional scale. In addition, this approach will be useful when greater understanding of a particular class of anthropogenic stressors (e.g., specific types of effluent) is required to improve regulatory guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".