🤖 AI Summary
This study addresses the lack of effective methods for detecting potential fabrication or non-random patterns in raw numerical research data. To this end, it proposes a progressive digital randomness screening framework (FDRS) that integrates statistical tests with interpretable machine learning models—specifically elastic net logistic regression and random forests—to enable fine-grained risk assessment and stratification of datasets. The framework uniquely combines multidimensional features of decimal-digit structure, a progressive subsampling strategy, and metrics including univariate and joint digit tests, Cramér’s V, entropy, Kullback–Leibler divergence, and a digit preference index. In internal validation, FDRS achieves an AUC of 0.984 and accuracy of 92.7%, and demonstrates strong performance in distinguishing high- from low-risk real-world datasets, substantially enhancing the efficiency of manual verification.
📝 Abstract
Raw numerical datasets remain less systematically examined in integrity screening than images, plagiarism, or summary-statistic inconsistencies. We developed the Fabrication-risk Digit Randomness Screening model (FDRS), a statistical and machine-learning framework for detecting non-random digit-pattern irregularities in numerical research data. FDRS integrates single- and joint-decimal-digit tests, Cramer's V, entropy metrics, Kullback-Leibler divergence, digit-preference indices, progressive subsampling, and semi-supervised risk scoring. It was evaluated using an instrument-derived enzymatic absorbance dataset (RawData, n=253) and a blinded manually simulated irregular dataset (ErrData, n=255). RawData showed no significant deviation in single third-decimal-digit analysis, whereas ErrData showed a significant deviation. In joint third-fourth decimal digit analysis, ErrData showed higher Cramer's V, lower normalized entropy, higher KL divergence, and a more persistent progressive-subsampling deviation signal. In internal validation, Elastic-net Logistic Regression achieved the highest AUC (0.98395) and lowest Brier score (0.048439), while Random Forest achieved the highest accuracy (0.926667) and balanced accuracy (0.935). RawData received a low ensemble risk score of 0.124627 and was classified as Grade 0; ErrData received a score of 0.740760 and was classified as Grade 3. External real-world benchmarks supported graded risk stratification: three datasets without identified public post-publication concerns were classified as Grade 0 or 1, whereas two datasets from publicly questioned or institutionally handled articles were classified as Grade 2 or 3. FDRS can prioritize raw numerical datasets for further review by integrating interpretable statistical and machine-learning features. It is an auxiliary digit-structure screening tool, not standalone evidence of fabrication or misconduct.