Consensus Building in Group Decision-Making for the Risk Assessment of Wind Farm Projects
Bibliographic record
Abstract
Infrastructure projects for harnessing renewable energy (e.g., wind farm projects) have recently gained popularity because of their low adverse impact on the environment. However, it is challenging to perform risk assessments for these projects because data are either scarce or of low quality. Therefore, risk assessments for renewable energy infrastructure projects must rely on expert knowledge and can be treated as multi-criteria group decision-making (MCGDM) problems. In group decision-making problems, consensus must be built between individual decision makers who each supply their own preference indices for decision alternatives. This paper introduces a novel technique for consensus building in MCGDM problems using the principle of justifiable granularity, thereby producing an interval-valued fuzzy set that represents the aggregated value of the preference indices assigned to decision alternatives by decision makers. The preference indices obtained from each expert are realized through the analytic hierarchy process (AHP). In this paper, the introduced MCGDM technique is used to assess risk for wind farm projects. First, a context-specific work breakdown structure for wind farm projects is developed. Second, construction work packages are ranked based on how much they contribute to the overall risk or uncertainty involved in achieving the project objectives of time, cost, quality, and safety.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".