Exploring the Impacts of Climate Change and Water Conservation Attitudes on Urban Water Supply in the Colorado River Basin
Bibliographic record
Abstract
Abstract Under intensifying climate change, demand management policies are likely to become increasingly important means for increasing the resilience of surface water systems during scarcity periods. Yet, there is little knowledge focused on how these policies might function in the face of climate change. In this sense, understanding the likely effectiveness of demand management policies under climate change is critical for ensuring sustainable and equitable long‐term planning of water systems. Here, we build an agent‐based model to evaluate the reservoir storage in the cities of Denver (Colorado), Las Vegas (Nevada), and Phoenix (Arizona) under climate change and demand management scenarios. The results indicate that in some scenarios, demand management policies can counteract the negative impacts of climate change. In others, however, the demand management policies have minimal impact, suggesting that demand management alone is not always enough to counteract climate change. Overall, the model can be used to test other demand management policies, while also serving as a basis for improved understanding of the interactions between climate change, water conservation attitudes, and demand management policies within the Colorado 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".