A weight-of-evidence approach for Northern river risk assessment: Integrating the effects of multiple stressors
Bibliographic record
Abstract
Abstract Northern river ecosystems are subject to a variety of stressors having multifaceted (and sometimes opposing) effects, making interpretation at a regional scale difficult. We have addressed this problem by using a weight-of-evidence approach that combines analysis of field data (to determine patterns) with experimental hypothesis testing (to determine mechanisms). Two of the more important sources of aquatic impacts in western Canada are pulp mill and municipal effluents. Their regional impacts on benthic biota were evaluated for two major river systems, the Thompson and Athabasca rivers, using an integrative approach. In the more southerly Thompson River, several lines of evidence (including field and laboratory experiments, field sampling over a 20-year period, and isotopic analysis) led to the conclusion that, although some toxic effects were apparent, these effects were usually masked by the (sometimes excessive) nutrient enhancement effects of these effluents, sometimes via novel pathways. Furthermore, analysis of the data revealed a fairly delicate balance in effluent treatment involving trade-offs between the negative effects of toxic contaminant loading versus a switch to a more eutrophic community. In the more northerly Athabasca River, effluent effects can be modified by the added impact of another stressor: widespread winter freeze-up, which prevents reaeration of oxygen-depleted waters, coupled with low dissolved oxygen levels in the substratum where benthic invertebrates are found, resulting in a net shift in effluent effect from one of nutrient enhancement to a more inhibitory effect. Advantages to applying formalized causal criteria, as outlined in this weight-of-evidence approach, include helping to tie together diverse assemblages of data on the effects of multiple stressors and identifying important informational gaps, thus making ecological risk assessments more rigorous and robust.
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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 | 0.210 | 0.323 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.037 | 0.013 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".