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Python - Entropy-based bias correction for measurement,

The attached article, "The Entropic Measurement Revolution" by Raphael Constantinis, introduces a groundbreaking scientific framework where measurement bias is no longer considered a mere error, but a fundamental thermodynamic signature of any measurement process. By uniting principles from quantum mechanics, thermodynamics, and information theory, this work demonstrates that every act of measurement generates entropy—and that this entropic cost can be explicitly measured, corrected, and transparently reported. Experimental validation, practical examples (AI, cosmology, medical imaging), and philosophical implications are discussed, calling for a shift toward more honest and responsible scientific practices. All concepts and methods are released into the public domain for universal use.

Metrology

Geneva

@BTFentropiceffect

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