Time–Scalar Field Theory (TSFT) proposes that spacetime structure arises from a scalar field Θ (x^μ) governing temporal coherence. In this work, we present a formal consolidation of the framework, focusing on its ability to reproduce key features of relativistic gravity under explicitly defined conditions. We construct a covariant metric from scalar temporal gradients and derive the associated connection coefficients and geodesic equations. Analysis of the minimal gradient-squared metric ansatz, gμν = ημν + α∂μΘ∂νΘ, shows that it fails to reproduce observed weak-field gravitational scaling, yielding corrections that decay too rapidly and an effective post-Newtonian parameter γ ≈ 0. This establishes that additional structure is required for physical viability. We demonstrate that a consistent weak-field limit is obtained when observable geometry responds linearly to the scalar field. In this formulation, the metric takes the form g00 = − (1 + 2λΘ), gij = (1 − 2ζΘ) δij, with the condition ζ = λ required to recover the observationally supported value γ = 1. Under this mapping, the scalar field solution for a static source naturally produces the correct Newtonian potential scaling and reproduces the leading-order optical sector of gravity, including light propagation and Shapiro time delay. The analysis identifies metric closure as a nontrivial component of the theory: the scalar field equation □Θ = S does not uniquely determine observable geometry without additional coupling structure. TSFT is therefore reframed as a constrained framework in which scalar temporal dynamics encode gravitational behavior under specified coupling conditions. Finally, we present a direct empirical pathway for testing the theory using precision timing systems, including Global Navigation Satellite System (GNSS) residuals, which provide quantitative bounds on deviations from General Relativity. These results establish TSFT as a testable scalar-field-based approach to gravitation, with clearly defined success conditions and identifiable failure modes.
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Jordan Gabriel Farrell
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Jordan Gabriel Farrell (Thu,) studied this question.
www.synapsesocial.com/papers/69be37b96e48c4981c6778dc — DOI: https://doi.org/10.5281/zenodo.19113809