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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".