Site selection of medical waste disposal plants: A social network group decision-making framework with incomplete Pythagorean fuzzy preference relations
Why this work is in the frame
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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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it