Neutrosophic Analysis of the Experimental Data using Neutrosophic Latin Square Design
Bibliographic record
Abstract
While dealing with the observed or measured data in surveys or experiments, it is not uncommon to deal with vague, incomplete, and imprecise information for whatever reasons. In this regard, researchers have proposed various emerging approaches such as fuzzy, intuitionistic fuzzy and neutrosophic logic and analysis, which provide better understanding, analysis and interpretations of the data. Neutrosophic logic is an extension of fuzzy logic where a variable x is described by triplet values, i.e., x ti f = , , ( ) , where t is the degree of “truth”, f is the degree of “false” and i is the level of “indeterminacy” AlAita, Abdulrahmanand Aslam, Muhammad (2023). A neutrosophic data x can be expressed as x = d + i, where d is the determinate (sure) part of x, and i is the indeterminate (unsure) part of x. Experimental design and analysis is a systematic, rigorous approach to problem solving that applies principles and techniques at the data collection stage so as to ensure the generation of valid, defensible, and supportable conclusions. Latin square designs (LSDs) are used to compare treatment factor levels represented by the Latin letters and using two blocking factors in rows and columns to simultaneously control two sources of nuisance variability. In this paper, we will define a neutrosophic Latin square design (NLSD), neutrosophic LSD model and consider the neutrosophic analysis of the NLSD experimental data for testing the abrasion resistance of rubber-coated fabric in a Martindale wear tester.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".