Intuitionistic Fuzzy Quasi-Supergraph Integration for Social Network Decision Making
Bibliographic record
Abstract
This study explores the complexities of intuitionistic fuzzy (hyper) graphs, considering them as complex (hyper) networks, and presents a unique idea for intuitionistic fuzzy (quasi) superhypergraphs. The extension considers intuitionistic fuzzy superhypergraphs to be complicated superhyper networks to establish particular and general links between labeled items. These intuitionistic fuzzy (quasi) superhypergraphs arrange labeled object groups and analyze them in several relational aspects at the same time, including part-to-part, part-to-whole, and whole-to-whole groupings. The research investigates the characteristics of intuitionistic fuzzy (quasi) superhypergraphs utilizing positive real numbers, such as valued intuitionistic fuzzy (quasi) superhypergraphs and their complements, permutation-based isomorphism notation, and isomorphic (self-complemented) valued intuitionistic fuzzy (quasi) superhypergraphs. It also presents the concept of impact membership value for intuitionistic fuzzy (quasi) superhypergraphs and demonstrates how it may be used to solve real-world problems. Finally, the research demonstrates the use of intuitionistic fuzzy valued quasi superhyper graphs in addressing social network analysis, emphasizing their practical use.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".