This case study presents a focused comparative analysis between two Solar System objects with distinct dynamical characteristics: Ceres (main-belt asteroid) Callisto (moon of Jupiter, used as an operational negative control) The analysis is conducted using the Predict_v3 framework, applied to high-resolution orbital data from JPL Horizons (NASA), entirely independently of any predefined theoretical model. Objective The purpose of this case study is to verify whether the framework is capable of: detecting emergent structure within the data distinguishing between presence and absence of signal maintaining consistency between FULL and REAL datasets satisfying explicit falsifiability criteria Methodological Approach The analysis follows the same principles as the general framework: strict separation between data and interpretation use of Trinamic metrics (Δt, Ea, kt, μt, §) as operational transformations fully traceable and reproducible pipeline validation based on coherence between FULL and REAL Role of the Two Objects The two objects serve distinct methodological roles: Ceres → positive case, where emergent structure is detectable Callisto → negative control, where no significant structure is expected This configuration enables a direct test of the framework under contrasting conditions. Main Result The case study demonstrates that: in the case of Ceres, statistically significant structures emerge in the case of Callisto, the system does not produce detectable structure This behavior is consistent with the defined falsifiability criterion: if the REAL dataset is statistically indistinguishable from a random distribution relative to FULL, the method does not produce valid results Interpretation The comparison between the two objects shows that: the framework does not artificially generate structure structure emerges only when present in the data the method correctly distinguishes between: structured dynamics non-structured dynamics Scientific Positioning This case study does not demonstrate a physical theory. It demonstrates instead that: the same method, applied to different datasets, produces different behaviors in a consistent and verifiable way The Trinamica CP364 framework is used as a descriptive tool, not as an a priori assumption.