Neutrosophic Selective Separability

We develop a confidence-sensitive neutrosophic framework for selective separability. Single-valued neutrosophic sets are evaluated through an admissible aggregation operator Φ(T, 1 − I, 1 − F), so that the conservative minimum is only one possible choice. We first study uncertain dense data over an ordinary topology and prove operator-independent transfer results for M-, H-, R-, and S-separability, while showing that the admissible witnesses remain genuinely confidence-sensitive. We then pass to single-valued neutrosophic topological spaces and distinguish strong intrinsic density, which reaches the full confidence height of every effective open set, from weak intrinsic density, which only requires positive confidence overlap. The weak and strong notions differ for individual neutrosophic sets, although both recover the expected classical space classes on crisp-induced topologies. To keep countable network weight meaningful, we introduce countably calibrated neutrosophic networks based on a dense rational set of point profiles. We also formulate both trace-based and genuinely graded selection principles controlled by single-valued neutrosophic filters on the index set. Finally, we compare the minimum and product confidence aggregators, discuss numerical resolution thresholds for intrinsic selection, and derive consequences for Fr´echet–Urysohn and function spaces.