Appendix A provides Python code for NPF/CNI calculation (raw score, linear/sigmoid normalisation, CNI aggregation) and simulation parameters. Appendix B gives a decision tree for selecting the sigmoid steepness parameter k based on cultural context (individualist vs. collectivist), with sensitivity analysis guidance. Both are theoretical tools; no validation is claimed.

What proto‑awareness looks like in real products, research labs, and policy. Shows how measurable awareness changes AI assistants, reproducibility checks, regulatory approvals, and public access to knowledge.

A public exploration of proof‑decay in science and AI. Shows how knowledge ages like bread, why claims need expiry dates, and how GRM treats every result as a living, perishable object with renewal rituals.

How the Gradient Reality Model (GRM) becomes a portable standard. Introduces the seven‑element claim template, registry schema, badge rubric, D.4 lineage logs, and day‑one adoption checklist. Written for labs, regulators, and any team wanting to adopt GRM.