The S64 Master Code: A Cross-Continental Metrological Framework for Megalithic Architecture (Giza & Puma Punku)

This paper presents a metrological investigation into a hypothesized modular base unit of u = 64 mm within megalithic structures at the Giza Plateau (Egypt) and Puma Punku (Bolivia). Utilizing a rigorous statistical methodology, including a u-variation sensitivity test (u_var ∈ [60, 70] mm), the study identifies a significant clustering of dimensions at rational and binary-fractional multiples of 64 mm. The results indicate a unit-specific signal that collapses upon a ±1.5% deviation from the base unit. Furthermore, a strong correlation between material density (igneous vs. sedimentary rock) and metrological conformity is established. The study remains strictly descriptive, identifying a statistical anomaly that warrants further sub-millimeter in-situ verification. Dataset & Methodology The analysis is based on a comparative dataset of high-precision architectural measurements from selected structures at the Giza Plateau (Egypt) and Puma Punku (Tiwanaku, Bolivia), derived from established archaeological documentation, notably the works of Flinders Petrie and Jean-Pierre Protzen. The study evaluates the presence of a hypothesized modular base unit defined as u = 64 mm through dimensional normalization. Each architectural length L is transformed into a scaling coefficient according to the relation k = L / u. Resulting k-values are evaluated for proximity to integer, rational, or binary-fractional numbers. Measurements are classified into conformity tiers based on absolute deviation from these target values: Tier 1 (≤ 1 %), Tier 2 (≤ 2 %), Tier 3 (≤ 5 %), and non-conformant (> 5 %). To assess robustness against arbitrary unit selection, a u-variation sensitivity test is applied. The base unit u is systematically shifted from 60 mm to 70 mm in fixed increments, and the resulting conformity distributions are analyzed to identify localized clustering or peak behavior. This procedure serves as a falsification test for random or scale-agnostic distributions. Material correlation is examined by grouping measurements according to lithology, distinguishing between igneous rocks (e.g., granite, andesite) and sedimentary rocks (e.g., limestone). This allows assessment of whether metrological conformity correlates with material hardness, preservation quality, and resistance to post-depositional deformation. Results The analysis reveals a statistically significant clustering of rational and binary-fractional scaling coefficients at u = 64 mm. This clustering manifests as a distinct and isolated peak in the u-variation sensitivity test. When the base unit deviates by approximately ±1.5 %, the clustering rapidly collapses, and the distribution converges toward stochastic noise comparable to neighboring unit values. High conformity rates are predominantly associated with igneous materials, while sedimentary limestone exhibits substantially lower conformity across all tiers. Identical or near-identical k-values recur across geographically distant sites, indicating cross-site consistency of the detected modular signal. Discussion The narrow-band peak centered at 64 mm is inconsistent with expectations from random clustering, which would produce broader or shifting maxima across adjacent unit variations. The observed dependence on material class further supports the interpretation that the signal is intrinsic to preserved architectural geometry rather than a statistical artifact of measurement selection. Limitations of the study include reliance on legacy measurement datasets and the absence of modern sub-millimeter in-situ scans. Accordingly, the findings are presented strictly as a statistical anomaly detection, without inference of intentional design, function, or cultural attribution. Conclusion A reproducible, unit-specific metrological anomaly centered at 64 mm is detected across multiple megalithic contexts. Within the tested parameter space, the results cannot be satisfactorily explained by random distribution. No functional, technological, or historical interpretations are proposed. Further verification using high-resolution in-situ measurement techniques, such as LIDAR or structured-light scanning, is required to confirm or falsify the detected signal.