Optimizing Public Management of Urban Water Levels: A Fuzzy Comprehensive Evaluation of Stakeholder Satisfaction in Lake Ontario
Bibliographic record
Abstract
Water level management in urban water bodies involves balancing ecological conservation with economic activities. Effective management strategies to mitigate the adverse effects of water level fluctuations have been identified as a key task by decision-makers. This study focuses on Lake Ontario, aiming to provide decision support through the construction of a fuzzy comprehensive evaluation model that incorporates the needs and satisfaction of various stakeholders, including shipping companies, residents, and environmental organizations. The evaluation model was applied to assess actual water levels and a simulated control scenario in 2017, yielding the following conclusions: the water level control strategy was effective, with a marked increase in stakeholder satisfaction in most months and a significant reduction in dissatisfaction levels. This research breaks through the limitations of traditional water level management evaluation methods by transforming complex and ambiguous stakeholder demands into specific, actionable evaluation indicators. Managers can use the comprehensive monthly data evaluations to assess the effectiveness of control strategies and make targeted adjustments. This provides a new perspective for managing Lake Ontario and other similar urban water bodies.
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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 | 0.001 | 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 | 0.000 | 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) | 0.006 | 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 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".