Using an Integrated Participatory Modeling Approach to Assess Water Management Options and Support Community Conversations on Maui
Bibliographic record
Abstract
The purpose of this study is to provide an integrated analysis of water distribution on Maui and the cross-sectoral impacts of policies and regulations aimed at rejuvenating and sustaining the deep-rooted culture on the island. Since the water diversion system was implemented in 1876 on the island of Maui, there has been contention among local interest groups over the right way to manage and allocate this precious resource. There is also concern over the availability of the precious resource in the long term, as the demand for water is expected to exceed the potential supply of water on Maui by 2020. This paper analyzes various long run scenarios of policy options presently being discussed on Maui. By collaborating with local experts, business leaders, and community members, to develop a tool that facilitates policy formulation and evaluation, informed decisions can then be made by the local community to ensure sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".