Integrating mesocosm experiments with field and laboratory studies to generate weight-of-evidence risk assessments for large rivers
Bibliographic record
Abstract
Abstract Regional assessments on large rivers often are 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. Our 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 the Fraser River, British Columbia, Canada, indicated that the major effect of present effluent discharges has been one of nutrient enrichment and stimulation of food web productivity. In fact, the Fraser River study suggests that small increases in effluent concentration in the river may produce negative ecological effects because of contaminant stresses. 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. 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 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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".