Commentary on “A reply to a Note on the paper “A simplified novel technique for solving fully fuzzy linear programming problems””
Bibliographic record
Abstract
Bhardwaj and Kumar (J. Optim. Theory Appl. 163 (2), 685-696 (2014)) pointed out that Khan et al. (J. Optim. Theory Appl. 159 (2), 536-546 (2013)) have assumed some mathematical incorrect assumptions in their proposed method for solving fully fuzzy linear programming problems (FFLPPs). However, Khan et al. (J. Optim. Theory Appl. 173 (1), 353-356 (2017)) replied that their proposed method is valid and no mathematical incorrect assumptions have been considered. The aim of this commentary is to make the researchers aware that Khan et al. (J. Optim. Theory Appl. 173 (1), 353-356 (2017)) are misleading the others. In actual case, Khan et al. (J. Optim. Theory Appl. 159 (2), 536-546 (2013)) have considered mathematical incorrect assumptions in their proposed method due to which some of the elements of the optimal simplex table, obtained by Khan et al. (J. Optim. Theory Appl. 159 (2), 536-546 (2013)), are not triangular fuzzy numbers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.064 | 0.074 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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 source (direct Gemma or distilled Codex), 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".