Resilience and Law as a Theoretical Backdrop for Natural Resource Management: Flood Management in the Columbia River Basin
Bibliographic record
Abstract
The 1964 Columbia River Treaty entered by the United States and Canada for mutual benefits in flood control and hydropower generation is under review in anticipation of expiration of certain flood control provisions in 2024. This Article asserts that nonstructural measures should be the primary focus of new expenditure on flood risk management in the Columbia River Basin over the next sixty-year period of treaty implementation to align flood risk management with management for ecosystem resilience. Resilience is the measure of the capacity of a system to maintain important functions, structures, identity, and feedback through adaptation in the face of a disturbance. Water basin governance can enhance or detract from ecosystem resilience, thus affecting the resilience of the combined social-ecological system. Floodplains provide important ecosystem functions not only as natural storage in flood risk management, but also to aquatic ecosystem resilience in general and salmonid habitat in particular. From the perspective of the social system, reliance on multiple geographically widespread locations for natural storage reduces the risk of crisis in the face of collapse of a single flood-control structure. These concepts have broad applicability to any major river basin with high hydrologic variability, and the Columbia River Basin faces a unique opportunity to employ them. Columbia River Treaty review combined with a public desire for improved ecosystem function presents an opportunity to enhance ecosystem resilience outside the emotional crisis management that ensues following a flood. Phased movement from sole reliance on centralized storage-based flood management by incremental addition of more diffuse nonstructural measures will enhance the social-ecological resilience of the Columbia River Basin
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".