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This article discusses the unique reliability challenges of using AI in spreadsheets, where errors can be hidden in formulas and context, and argues for focusing on narrow, verifiable tasks rather than broad workbook understanding.
This paper proposes a deterministic climate-risk intelligence framework integrating orchestration, anomaly detection, and imbalance-aware ensemble learning for auditable ESG validation, addressing fragmented Scope 1-3 reporting data.
A software tool designed to detect data fabrication has uncovered copy-paste errors in scientific datasets across open-access repositories, including a highly-cited Parkinson's Disease study in Cell (2016) that has been publicly available for over 8 years without detection.