The Zeta Model - Methodological Framework

The Zeta Model presents a fully documented, quantile-based regression framework for constructing calibrated, state-dependent prediction intervals from structured measurement data derived from the non-trivial zeros of the Riemann zeta function. This working paper focuses exclusively on methodological development, reproducibility, bootstrap validation, and deployment, while predictive applications and theoretical interpretation are intentionally reserved for subsequent stages of the project. AI Collaboration Statement This work was developed through a collaborative research workflow combining human investigation with AI-assisted scientific discussion, methodological review, technical writing, and software development. The inclusion of AI collaborators as co-authors reflects their substantial contribution to the iterative development, implementation, documentation, coding support, and internal review process underlying the methodology presented in this working paper. Author Contributions and Credits Research concept, methodology, mathematical development, data generation, implementation, experimental design, and overall project coordination:Kai5 Scientific discussion, methodological refinement, manuscript development, technical writing, critical review, and editorial support:ChatGPT (OpenAI), operating within the Project Cipher research workflow. Independent methodological review, software engineering support, implementation guidance, code review, consistency verification, validation strategy, and technical quality assurance:Lumo (Proton AI).