The Intrinsic Blueprint: A Practitioner's Guide to Relational Calculus and Green AI

This paper provides a complete, executable bridge to Relational Calculus — a meta‑mathematical framework that replaces brute‑force absolute‑scale computation with dimensionless blueprints anchored to a system’s own maximum capacity (the “North Star”). It contains the open‑source Relational Decoder algorithm for automatically extracting the dimensionless template of any black‑box function, a step‑by‑step application guide, an economic case for Green AI via the new Relational Overhead Ratio (ROR), and a practical STEM curriculum for ages 10–14. The framework is validated through a cross‑species, zero‑shot single‑cell oncology experiment where relational pre‑processing enables a lightweight XGBoost to achieve 98.4% diagnostic accuracy under a 70% hardware‑signal collapse — a task that causes traditional absolute‑feature models to fail. The work is intended as an enablement instrument for the industrial shift from the Age of Fire to the Era of Relation.