Water Resources Planning through Group Model Building in the Okanagan Valley, British Columbia, Canada
Bibliographic record
Abstract
Research provides evidence that managing water resources in the Okanagan Basin in south-central British Columbia, Canada, will become increasingly challenging in the future. Climate change will likely alter water supply patterns, and warmer temperatures will result in increased agricultural and residential demand. At the same time, rapid population growth will also result in increased residential demand. The combination of these changes increase future risk of water supply falling short of water demand. In the previous phase of this project, management strategies were evaluated qualitatively. To provide decision support, future scenarios must be combined with a more rigorous evaluation of management options. One way to bridge the gap between technical information and policy implementation is through actively engaging the region's water community in a shared learning process. We are accomplishing this through a group model building process, which facilitates learning through active development of a high-level scoping model. The objectives of the modeling process include: (1) Creating a basin-wide water balance and assessing the impacts of climate change relative to other stresses; (2) Identifying and capturing those aspects that are strongly connected to water resources, such as land use; (3) Evaluating adaptation strategies for sustainable water management; and (4) Creating a tool that can be used for public education. The process includes a series of workshops over a period of one year. Participants, who include water managers, planners, political leaders and a diverse range of related interests, will share and negotiate their mental models using system dynamics as a communication tool. A single model will be constructed in STELLA language and supported with existing data. Issues such as flood control, fish habitat, forest management, agricultural and domestic water use, and environmental stewardship may be represented. Adaptation strategies will be included and tested for effectiveness of meeting goals as well as for cost.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".