Evaluating the Consistency of Neutrosophic Data Using Various Statistical Distributions: Comparative Studies and Applications
Bibliographic record
Abstract
In this paper, we primarily have given neutrosophic coefficient of variation, robust neutrosophic coefficient of variation concern to interquartile range, and robust neutrosophic coefficient of variation concern to median absolute deviation. Following the introduction, we have explored the methods of neutrosophic coefficient of variation, which is an effective method for modeling data that is fuzzy, imprecise, and uncertain. For the comparative study, we have given numerical studies based on neutrosophic distributions including discrete and continuous distributions. First, we have compared all three neutrosophic coefficient of variations and then have given the comparative study for all neutrosophic distributions for these neutrosophic coefficient of variations. Also, we have given real data analysis on climate data to highlight the impact of the neutrosophic coefficient of variations. We found that neutrosophic coefficient of variations NCV and based on IQR have near about similar values while the neutrosophic coefficient of variation based on MAD has higher values than other two for all samples and distributions. Further, we observe that with increasing the sample values all three neutrosophic coefficients of variations also increase for all the distributions and provide a general framework over classical methods of coefficient of variations, and the graphical representations also clarify this.
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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 | 0.047 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".