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This paper introduces the Hybrid-to-NeSy (H2N) framework, which systematically translates hybrid mechanistic-data-driven models into neuro-symbolic AI designs, enabling the derivation of metrics for structural violation and belief dispersion as measures of epistemic uncertainty in the mechanistic part.