The Rosetta Stone of Temporal–Structural Analysis Conceptual Alignment Between Observations, Data Pipelines, and Extended Time Models

Recent observational datasets increasingly reveal temporal and structural anomalies that cannot be consistently interpreted within a single theoretical or operational framework. While classical physics provides established measurement formalisms and data pipelines offer robust numerical processing, a semantic and conceptual gap persists between raw observations, operational metrics, and higher-level interpretative constructs. This work introduces a Rosetta Stone framework for temporal–structural analysis. The framework functions as a conceptual alignment layer designed to map classical physical notions, operational data descriptors, and extended temporal constructs into a shared and unambiguous language.Its purpose is not to propose new predictive laws, computational methods, or analytical pipelines, but to define stable correspondences that enable coherent interpretation across heterogeneous analytical domains. The framework explicitly distinguishes three epistemic levels — observational, operational, and interpretative — and clarifies their respective roles and boundaries. By establishing precise semantic correspondences between commonly used terms such as residuals, coherence, stability, and persistence, and their operational and extended temporal counterparts, the framework: reduces ambiguity, prevents category errors, limits interpretative driftin the analysis of complex datasets. No algorithms, implementation details, parameter thresholds, computational recipes, or end-to-end analytical workflows are disclosed. The content is intentionally non-operational and non-predictive.The document serves exclusively as a linguistic and conceptual reference layer, ensuring that independently generated datasets and analyses can be read, compared, and discussed within a coherent interpretative space. This Rosetta Stone is released as a prior-art conceptual document. It is intended to provide foundational semantic grounding for ongoing and future observational work, in which data precede theory and interpretation remains explicitly separated from measurement and processing.