A Participatory Approach to Development of a Decision Support Tool
Bibliographic record
Abstract
Effective decision-making in water management must consider both the physical characteristics of the system and the social, political, and institutional aspects. These latter aspects cannot be understood through scientific assessment, but are familiar to local residents and water interests. A decision support tool will be developed to assist in long-term water resources planning activities in the Okanagan Basin in British Columbia, Canada. The model will be created in a system dynamics platform, and will integrate technical hydrologic and climate change model results with institutional and social aspects. An advisory committee of local planners and decision makers will play an important role in the development of the model; they will provide information for the institutional and social aspects, and they will help to discern what level of complexity will provide the best results for their planning activities. This close involvement with local experts will ensure that the completed model will be useful for the community. Furthermore, the model development process itself will be a format for shared learning about water management in the Okanagan.
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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 | 0.070 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".