A note on “An algorithmic approach to solve unbalanced triangular fuzzy transportation problems”
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Muthuperumal et al. (Soft Comput (2020) 24: 18689–18698) proposed two different approaches to find an initial fuzzy basic feasible solution of unbalanced triangular fuzzy transportation problems (unbalanced transportation problems in which each parameter is represented by a triangular fuzzy number). Since, Muthuperumal et al. have claimed that less computational efforts are required to apply their proposed approaches as compared to other existing approaches. Therefore, in future, other researchers may use Muthuperumal et al.’s approaches to find an initial fuzzy basic feasible solution of real-life unbalanced triangular fuzzy transportation problems. However, it is observed that in actual case, the initial fuzzy basic solution, obtained by Muthuperumal et al.’s approaches, is not a fuzzy feasible solution. Hence, it is not appropriate to use Muthuperumal et al.’s approaches. The aim of this note is to point out the inconsistencies that exist in Muthuperumal et al.’s approaches.
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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 | 0.001 | 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 | 0.001 |
| 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