Relation on Hesitation in Intuitionistic Fuzzy Sets and Decision Making
Bibliographic record
Abstract
Hesitation defined in intuitionistic fuzzy sets (IFSs) is applied to analyze uncertain information. It plays a crucial role in decision-making processes, capturing the intermediary position between supportive and opposing information. Each membership function is allocated to independent vector coordinate. Hence, hesitation is expressed by the graphical illustration in a two-dimensional space. The result shows distinct comparison between IFSs, and it provides effective information on the hesitation. By the defined membership and nonmembership coordination, their degree are represented by graphical illustration which are composed of four quadrants; quadrant II is directly related with the affirmative degree; quadrant IV is relevant to dissent. Furthermore, hesitation degree is also shown by cross area. With the proposed hesitation measure, Korean congress election example is applied and are reported the reasonable results with the proposed hesitation measure.
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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 | 0.000 | 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".