Integrating causation in investigative ecological weight of evidence assessments
Bibliographic record
Abstract
Abstract Weight of evidence (WOE) frameworks integrate environmental assessment data to reach conclusions regarding relative certainty of adverse environmental effects due to stressors, possible causation, and key uncertainties. Such studies can be investigative (i.e., determining whether adverse impact is occurring to identify a need for management) or retrospective (i.e., determining the cause of a detected impact such that management efforts focus on the correct stressor). Such WOE assessments do not themselves definitively establish causation; they provide the basis for subsequent follow-up studies to further investigate causation. We propose a modified investigative WOE framework that includes an additional weighting step, which we term “direction weighting.” This additional step allows for the examination of alternative hypotheses and provides improved certainty regarding possible causation. To our knowledge, this approach has not been previously applied in investigative ecological WOE assessments. We provide a generic example of 2 conflicting hypotheses related to a mine discharging treated effluent to a freshwater lake: chemical toxicity versus nutrient enrichment. Integr Environ Assess Manag 2017;13:702–713. © 2016 SETAC Key Points A novel weight of evidence (WOE) approach has been developed for investigative ecological assessment with an example conceptual model involving a northern lake receiving treated effluent from a mine. Through the application of novel “direction weighting,” the WOE approach can provide a high level of discrimination between 2 competing hypotheses: toxicity due to metals and other substances, or nutrient enrichment. This WOE approach is broadly applicable to other investigative ecological assessment situations in which both nutrient enrichment and toxicological impairment are possible. The approach can also be used to assist in resolving other competing hypotheses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".