Scientific opinion classification based on discourse functions provides a structured semantic basis for analytical tasks such as gap identification and hypothesis generation. However, this task is uniquely challenged by the multi-label nature of scientific expressions and AIMRaD structural constraints. Existing LLM-based methods typically rely on direct label generation, which obscures decision logic, or treat discourse information as passive context rather than a structural prior. We propose OPINE, a multi-stage framework that reformulates classification as a controllable *scoring-calibration-refinement* pipeline. By decoupling textual evidence from decision logic, OPINE generates independent label-wise affinity scores calibrated by AIMRaD priors. To resolve the multi-label challenge, we introduce a quantile-based decoding rule to naturally capture co-existing roles, alongside a pairwise refinement mechanism to mitigate confusion between similar categories. We contribute a new benchmark of 18 discourse functions across diverse sections. Experimental results show that OPINE generally outperforms strong baselines, reaching F1 scores of 63.20%, 53.68%, and 63.22% under Micro, Macro, and Example settings, respectively. Our analysis reveals that integrating discourse structures as explicit priors is superior to conventional passive context integration, while pairwise refinement successfully mitigates confusion between functionally similar categories. The code and dataset are available at https://github.com/znoodle63/OPINE.