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Prosodic boundary detection has traditionally relied on manual annotations such as ToBI labels, which can be resource-intensive and are not always available in large speech corpora. This paper presents a weakly supervised framework that derives high-confidence prosodic boundary anchors from acoustic cues, without depending on manually labeled prosodic boundaries. We adopted a hierarchical anchor construction procedure in which long pauses were first used to identify a conservative set of boundary candidates, and pitch reset and energy reduction were then used to refine these candidates into progressively stricter anchors. These anchors were designed to emphasize precision over coverage and served as reliable positive instances for positive-unlabeled (PU) learning. By framing prosodic boundary detection as a boundary strength estimation problem, we employed PU learning to infer continuous boundary strength scores over all candidate junctures under weak supervision. Experiments on a large-scale Japanese speech corpus demonstrated that meaningful prosodic boundary patterns can be recovered from acoustic cues using PU learning, thus providing an interpretable, data-efficient approach to prosodic boundary modeling.