
This preprint presents the instrument-level validation of Structural SimilarityCoefficient (SSC) computation under a fully deterministic execution contract.Two independent distance pipelines—SciPy’s condensed pdist and NumPy’ssquare-distance → squareform route—are evaluated for cross-implementationequivalence using a pre-specified Δ-specification (mean < 0.07, max < 0.10).Under locked float64 precision, dependency pinning, OpenBLAS 0.3.30 insingle-thread mode, and fixed random seeds, cross-implementation differencesremain at machine-precision scale (≈10⁻¹⁶–10⁻¹²) across Euclidean, cosine, andcorrelation distances at N=1000, D=50. This study defines the deterministic execution contract, provides completereproduction instructions, and archives all outputs (ΔSSC metrics, statisticalsummaries, figures, and environment snapshots). The validated instrumentconstitutes the Instrument Core (I-1) of the Optics of Intelligence framework,supporting subsequent Observation (O-series) and Theory (L-series) studies. This archived version contains the exact code, data, and CI-generated artifactscorresponding to the publication-ready form of Kakushin Structural Theorypreprint 2025-144.
semantic-spatial correlation, scientific computing, numerical analysis, cross-implementation equivalence, Kakushin Structural Theory, structural similarity coefficient, Optics of Intelligence, reproducibility, SSC, Δ-specification, deterministic computation, instrument validation
semantic-spatial correlation, scientific computing, numerical analysis, cross-implementation equivalence, Kakushin Structural Theory, structural similarity coefficient, Optics of Intelligence, reproducibility, SSC, Δ-specification, deterministic computation, instrument validation
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