Science, Uncertainty, and Values in Ecological Restoration: A Case Study in Structured Decision‐Making and Adaptive Management
Bibliographic record
Abstract
Abstract This article demonstrates a structured and collaborative approach to decision‐making in the context of adaptive management experiments, using a case study involving the restoration of a hydrological regime in a regulated river in western Canada. It provides a framework based on principles of decision analysis for structuring difficult multi‐attribute decisions and building the trust and technical capacity needed to implement them. Participants included ecologists and fisheries biologists, government regulators, electric utility employees, and representatives of aboriginal communities. The case study demonstrates a values‐based approach to implementing adaptive management that addresses some of the long‐standing difficulties associated with integrating adaptive management into restoration decisions. It highlights practical methods for incorporating participants' values concerned with learning, cultural quality, and stewardship as part of developing a decision‐making and monitoring framework for restoration initiatives. It also provides an example of how to implement principles of meaningful consultation in a restoration context, with emphasis on ensuring that all voices and concerns are heard and meaningfully incorporated. Participants have adopted the framework as a model to guide future collaborative decision‐making processes involving Aboriginal communities, regulatory agencies, and other parties.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".