Integrated Hydro-Irrigation-Restoration Systems: Resolving a Wicked Problem in the Whychus Creek Watershed (Oregon, USA)
Bibliographic record
Abstract
There is an emerging problem of water scarcity associated with the water-energy nexus that has become even more complicated, and more acute, in many rural, irrigation-dependent farming and ranching communities in arid and semi-arid regions of the western U.S. and Canada. The combination of environmental laws, growing populations, increasing demand and higher costs for energy, globalized competition for agricultural commodities, and the spectre of climate change creates a wicked problem that challenges the efficacy of traditional water rights and water delivery systems, as well as the long-term sustainability of the resource-oriented communities and ecosystems involved. How might this wicked problem be resolved such that we simultaneously have more water for streams (ecological health) and growing populations, fewer fish passage obstructions, improved economic viability for working rural landscapes, more carbon free energy, adequate water quality, and improved reliability in water delivery for all water rights holders, while respecting and keeping existing water rights intact? This research analyzes the case of the Whychus Creek watershed in Oregon (USA), where an inclusive set of stakeholders collaboratively transformed the traditional irrigation system into an integrated hydro-irrigation-restoration system more fully responsive to the many different facets of the wicked problem associated with the water-energy nexus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".