Aggregation of Inputs from Stakeholders for Flood Management Decision-Making in the Red River Basin
Bibliographic record
Abstract
The Red River Basin in Canada faces periodic flooding where flood management decision-making problems often involve multiple objectives and multiple stakeholders. To enable more effective and acceptable decision outcomes, more participation in the decision-making process is required. A challenge is to obtain and use the diversified opinions of a large number of stakeholders where uncertainty plays a major role. In response to this challenge, a methodology has been proposed to capture and aggregate the views of multiple stakeholders using fuzzy set theory and fuzzy logic. Three possible response types: scale (crisp), linguistic (fuzzy) and conditional (fuzzy) are analyzed to obtain the aggregated input using Fuzzy Expected Value. The methodology has been tested for flood management in the Red River Basin using a generic case study. While the results show successful application of the methodology, they also show significant differences in preferences of the stakeholders as a function of location in the basin. Thus the paper provides alternative ways for collecting and aggregating the input of multiple stakeholders to assist the flood management decision-making process.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".