Managing beyond water: utilizing community well-being interviews in the Upper Yakima River Basin, USA, for climate change adaptation
Bibliographic record
Abstract
In contemporary, natural resources dependent and specialized communities, community well-being is connected to the environment. Understanding the local connections between community well-being and the environment can provide a more complete understanding of how to manage social-ecological systems and promote community resilience. Herein, we combine semi-structured community well-being interviews with hydrologic modeling using the variable infiltration capacity (VIC) model to suggest climate adaptation pathways for a diverse set of community interests. We found that community well-being across the Yakima River Basin was connected to water, snow, and the environment through recreation opportunities, aesthetics, livelihoods, and having clean water and air. Additionally, many community members noticed changes in snowpack conditions and were aware that snow conditions affect water resources and local agriculture. We identified that community concerns, resilience, and innovation centered around preserving the Yakima Valley’s historic and future potential as a regional and global agricultural and recreational hub. We discussed two case studies that highlight how climate adaptation plans can be expanded to include other groups, resources, and governance foci. The first case is about the social aspect of sustained days of high heat and the second is about an expressed false sense of security with snowmelt. We do this by incorporating modeled future projections of consecutive high heat days and snowmelt timing. By integrating well-being interviews with hydrologic modeling, we show how we can create more climate-adapted and resilient social-ecological systems that can preserve and maximize well-being in the context of changing environmental conditions. Other natural resources dependent communities also have connections between community well-being and the environment, thus a similar approach can be used in future research to explore the location-specific context.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".