Assessing System Resilience and Ecosystem Services in Large River Basins: A Case Study of the Columbia River Basin
Bibliographic record
Abstract
The economic life and the health of society depend on the services provided by large river basins. Throughout the world, widespread development and modification of river basins has resulted in highly stressed ecosystems and societal dependence on engineered services (i.e. the use of infrastructure such as dams and diversions to maximize certain uses of the river) that may be reaching their maximum capability in delivery. These water-based social-ecological systems (SES) are particularly vulnerable to climate change. The complexity of river basins is reflected not only in the biophysical system and the provisioning of ecosystem services, but in societal interaction with these systems, particularly water governance. In the face of change, water governance must become adaptive. Improvement in the capacity of these social-ecological systems to adapt through changes in governance begins with understanding the system-wide effects of past changes and the evolution of social interaction with the basin’s ecological system. As part of the Adaptive Water Governance Project, this article explores the resilience of the Columbia River Basin social-ecological system to climate change. It begins with an overview of its theoretical background and methodology, and proceeds to a basin characterization. The article then presents a resilience assessment of the basin following methods developed by Walker and Salt and by the Resilience Alliance, but modified to include ecosystem services concepts as a means to discuss system properties. This study takes place in the face of a key window of opportunity for change brought about by expiration of certain provisions of a Treaty between the United States and Canada, and the review process both countries have begun. Although focused on system-wide perturbation resulting from climate change as a thought experiment, this article will view that change in light of this current window of opportunity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".