Site selection of medical waste disposal plants: A social network group decision-making framework with incomplete Pythagorean fuzzy preference relations
Bibliographic record
Abstract
The rapid growth of the healthcare industry has led to an increase in medical waste, which poses significant public health and environmental challenges worldwide. Therefore, selecting the right location for medical waste disposal plants is crucial. To address this issue, especially when multiple decision-makers are involved, our research developed a social-network group decision-making (SNGDM) framework using incomplete Pythagorean fuzzy preference relations (ICPFPRs). First, targeting the missing information in ICPFPRs, we designed an estimation algorithm to derive complete Pythagorean fuzzy preference relations (CPFPRs). An information uniformity index (IUI) was defined based on the trust scores of experts and the degree of similarity among them within the Pythagorean fuzzy social network. Then, in the consensus-reaching stage, an minimum cost consensus(MCC) model was built to compute CPFPRs with acceptable consistency and group consensus levels. In detail, we introduced a determination method for unit adjustment costs by considering both the confidence level and social influence of experts. Next, the information aggregation and selection were conducted in light of the weights of experts, which were generated by integrating the consistency index, approximation degree, and trust scores. Finally, a numerical example of the site selection of a medical waste disposal plant was presented to validate the presented SNGDM framework. A series of comparison analyses were further carried out to demonstrate the advantages of our proposed method.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".