Neutrosophic Method for Identifying Extreme Values in Imprecise Data Using Median Absolute Deviation
Bibliographic record
Abstract
The traditional calculation of Z-scores for outlier detection is highly sensitive to extreme data points, making it unsuitable under conditions of uncertainty. In this study, we propose a novel approach to modify the Z-score method using neutrosophic statistics. Key statistical measures, including the median, neutrosophic standard deviation, and median absolute deviation, will be computed based on neutrosophic random variables. An extensive simulation study will evaluate the impact of varying uncertainty levels on the adaptation of Z-scores for outlier detection and their effectiveness in identifying outliers. Comparative analysis of Z-scores derived from different methods will also be performed. The proposed methodology will be applied to neutrosophic GG25 gray cast iron data, demonstrating its practical utility. We hypothesize that uncertainty levels will significantly affect Z-score computations and, consequently, outlier detection in the dataset.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".