Real-Life Applications of New Type Spherical Fuzzy Sets and Its Extension Using Aggregation Operators
Bibliographic record
Abstract
The purpose of this article is to present a novel approach to the multiple attribute decision-making problem (MADM) based on (l1, l2) spherical fuzzy sets (SFS). This is an extension of the SFS. As a result of this article, we will discuss the concept of spherical fuzzy weighted averaging (SFWA), spherical fuzzy weighted geometric (SFWG), generalized spherical fuzzy weighted averaging (GSFWA) and generalized spherical fuzzy weighted geometric (GSFWG). Here is a flowchart that shows how these operators are used in the algorithm we discussed. With the help of a numerical example, we illustrate the extended Euclidean and Hamming distance measures. Additionally, the SFN approach is characterized by idempotency, boundedness, commutativity, and monotonicity. These tools help you find the best option faster, simpler, and more conveniently. The result is a more precise conclusion and a more intimate relationship between (l1, l2). We compare some of the current models with those that have been proposed in order to demonstrate the dependability and utility of the models under investigation. The study also revealed fascinating and intriguing findings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".