Multi-Criteria Decision-Making Approach with Interval-Valued Intuitionistic Fuzzy Assessment for Green Supplier Evaluation and Selection
Bibliographic record
Abstract
<p>Becoming climate positive is the objective businesses, which have recently started to focus on and research about to curb the ever-increasing climate challenges while being profitable efficiently. In the pursuit of integrating environmental aspects into conventional supply chains, the concepts of green supply chain and supplier selection have emerged that become increasingly complex due to current economic globalization. In this research, we have proposed a green supplier selection methodology based on a comprehensive set of criteria and interval-valued intuitionistic fuzzy assessment of alternatives for realistic decisions. The three phases of the methodology constitute pertinent criteria selection incorporating environmental and social aspect, the assessment of alternatives in an interval-valued intuitionistic fuzzy environment through an integrated multi- criteria decision-making approach comprising best-worst method (BWM) and technique for order preference by similarity to ideal solution (TOPSIS) with cosine similarity measure and finally the sensitivity analysis. We have supported the proposed methodology by illustrative example considering a real-world scenario. </p>
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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 | 0.018 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".