A New Approach of Possibility Single-Valued Neutrosophic Set and Its Application in Decision-Making Environment
Bibliographic record
Abstract
The single-valued neutrosophic set (SVNS) is a widely known model for dealing with uncertain, conflicting, and indeterminate information. In practice, the SVNSs are very useful tools to be used in solving multi-criteria decision-making (MCDM), but in the process of processing by the three functions of SVNS the evaluation process for this handling disappears. To overcome this deficiency, we present in this work a new approach called possibility single-valued neutrosophic set (PSVNS) that differs from previous approaches. The implementation of this proposed approach in this work is based on giving each of the three functions in SVNS a fuzzy degree ranging between 0 and 1. As a result, firstly, the elementary notion of possibility single neutrosophic set is proposed, and some of its primary properties, i.e., subset, null set, absolute set, and complement are explored, as well as some numerical examples that explain the mechanism of the obtained results. Secondly, the basic set-theoretic operations i.e., such as extended union, the intersection of two PSVNSs, and the complement operation of PSVNS, as well as some relevant properties, are investigated, and numerical examples are provided to illustrate the mechanism behind these results. Lastly, the similarity measure between two PSVNSs is characterized with the help of an example. This technique of similarity measure is successfully used in decision-making to choose the appropriate college.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".