Full Cross-Domain Added Predictive Value of Vitology: Can Predefined Viability Metrology Improve Prediction Beyond the Strongest Domain-Specific Models Across Systems of Different Nature?

This article formulates an integrated protocol for testing the full cross-domain added predictive value of Vitology. The central question is whether a predefined and non-redundant metrology of system viability provides reproducible information about future system outcomes beyond the strongest established domain-specific scientific models. For each domain D_i, Model A_Di is defined as the strongest justified baseline incorporating all relevant established predictors, mechanisms, constraints, state variables, recovery indicators, robustness measures, safety information, control variables, and domain-specific knowledge. Model B_Di is defined as Model A_Di plus only those Vitological indicators that have been prospectively specified, independently measurable, and shown not to duplicate information already contained in Model A_Di. The general comparison is: Model B_Di = Model A_Di + Φ_V(D_i) where Φ_V(D_i) contains the admissible non-redundant Vitological components for that domain. Candidate components may include the viability profile Π_V, viability trajectory Γ_V, recovery and reserve information, thermodynamic–viability indicators, future viable continuation measures, Synchroauthenticity variables, and Norm-of-Harmony variables H_V, provided that each component satisfies its own preregistered non-redundancy criterion. For every domain: ΔP_Di = P_BDi − P_ADi where P represents a prospectively selected predictive-performance measure. Cross-domain support does not require identical physical variables, units, mechanisms, or numerical thresholds across domains. It requires reproduction of the structural relation: predefined viability information → added prediction of subsequent viability outcome after the strongest domain-specific scientific information has already been included. A single positive result provides only initial support. Replication within the same domain provides intra-domain support. Reproduction in a system of different nature provides cross-domain support. Prospective prediction of previously unseen data provides stronger evidence. Independent external replication provides the strongest support considered in the present framework. Strong non-support occurs if Vitological indicators cannot be defined independently of outcomes, duplicate established variables, fail to improve Model A, fail to replicate, or fail to transfer structurally across domains. I9 therefore does not attempt to declare Vitology a confirmed universal theory. It establishes a falsifiable architecture for determining whether a Dynamic Cross-Domain Viability Metrology has independent empirical value. Keywords Vitology; viability; cross-domain metrology; added predictive value; Model A; Model B; independent data; predictive validation; replication; reproducibility; viability profile; viability trajectory; recovery; reserve; maintenance cost; future viable continuations; Synchroauthenticity; Norm of Harmony; cross-domain invariant; empirical validation; falsifiability.