Possibility Fermatean Interval Valued Fuzzy Soft Set and Their Application to Decision Making Framework
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we introduce the theory of possibility Fermatean interval valued fuzzy soft (PFIVFS) set and its application to real life problems. The PFIVFS set is a generalization of Pythagorean fuzzy soft and soft set. We define some operations consist of complement, union, intersection, AND and OR. Notably, we show DeMorgan’s laws and associative laws and distributive laws are valid in PFIVFS set theory. We discuss the need to buy a laptop and find several stages for consumer goes through before purchasing a product. We propose an algorithm to solve the decision making problem based on soft set method. To compare PFIVFS set and Fermatean interval valued fuzzy soft (FIVFS) set for dealing with decision making problems, we find a similarity measure. Finally, an illustrative example is discussed to prove that they can be effectively used to solve problems with uncertainties.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 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 it