Mehar ranking method for comparing connection numbers and its application in decision making
Bibliographic record
Abstract
Kumar and Garg (Applied Intelligence, 2017, 10.1007/s10489-017-1067-0 ) pointed out the limitations of some existing methods for solving intuitionistic fuzzy multi-attribute decision-making (MADM) problems. Also, to overcome the limitations, Kumar and Garg proposed a connection number (CN) based method for solving intuitionistic fuzzy MADM problems. In this paper, it is shown that the ranking method, used in Step 5 of Kumar and Garg’s method for comparing connection numbers (CNs), fails to compare two distinct CNs. Hence, Kumar and Garg’s method fails to rank the alternatives of intuitionistic fuzzy MADM problems. Furthermore, to overcome the limitation of Kumar and Garg’s method, a new ranking method (named as Mehar ranking method) is proposed for comparing CNs.
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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 | 0.018 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".