The integration of ecological risk assessment and structured decision making into watershed management
Bibliographic record
Abstract
Watershed management processes continue to call for more science and improved decision making that take into account the full range of stakeholder perspectives. Increasingly, the core principles of ecological risk assessment (i.e., the development and use of assessment endpoints and conceptual models, conducting exposure and effects analysis) are being incorporated and adapted in innovative ways to meet the call for more science. Similarly, innovative approaches to adapting decision analysis tools and methods for incorporating stakeholder concerns in complex natural resource management decisions are being increasingly applied. Here, we present an example of the integration of ecological risk assessment with decision analysis in the development of a watershed management plan for the Greater Vancouver Water District in British Columbia, Canada. Assessment endpoints were developed, ecological inventory data were collected, and watershed models were developed to characterize the existing and future condition of 3 watersheds in terms of the potential risks to water quality. Stressors to water quality include sedimentation processes (landslides, streambank erosion) and forest disturbance (wildfire, major insect or disease outbreak). Three landscape-level risk management alternatives were developed to reflect different degrees of management intervention. Each alternative was evaluated under different scenarios and analyzed by explicitly examining value-based trade-offs among water quality, environmental, financial, and social endpoints. The objective of this paper is to demonstrate how the integration of ecological risk assessment and decision analysis approaches can support decision makers in watershed management.
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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 | 0.034 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".