An Integrated HFLTS and HF-TOPSIS Approach for Wind Turbine Site Selection Problem
Bibliographic record
Abstract
A vast majority of decision problems consist of conflicting criteria and non-dominating alternatives. To solve such kinds of decision problems, multiple criteria decision-making (MCDM) techniques have widely been used in many fields, so far. Moreover, hybrid decision-making approaches are also developed by the integration of different MCDM techniques in order to utilize the benefits of those methods. The main aim in this study is to introduce an integrated decision-making method for decision problems under uncertainty. Within this approach, Hesitant Fuzzy Linguistic Term Sets (HFLTS) and Hesitant Fuzzy Technique for Order Preference by Similarity to Ideal Solution (HF-TOPSIS) are integrated. The HFLTS method is used for the determination of criteria weights and decision alternatives are evaluated by using the HF-TOPSIS technique. The applicability of the method is demonstrated on an application of the wind turbine location problem.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".