Does Better Reprojection Mean Better 3D Reconstruction? Candidate- and Evidence-Stratified Evaluation for FLFM

In Fourier light-field microscopy (FLFM), does better reproduction of the camera image imply more faithful recovery of three-dimensional fluorescence structure? Reprojection fit supports selection when paired volume references are unavailable. We introduce SCOPE-FLFM to examine what this criterion reveals. Comparable-Candidate Stratification (CCS) organizes comparisons across model families, related architectures, and states from the same training run. Contract-Separated Evidence Validation (CEV) distinguishes supplied-view fit, excluded-view prediction, and normalized agreement with known synthetic objects. Using a synthetic FLFM corpus and reference volumes spanning beads, clusters, filaments, and mixtures, we find that pooled rank agreement weakens among close candidates and prospective choices depend on the scored target. Under the tested additive background, max-normalized reprojection SSIM increases while object Pearson decreases across all core learned states. A matched HYPER-derived Calibra-LF case study improves clean-corpus held-out SSIM and object Pearson on average while worsening other endpoints. SCOPE-FLFM links each score to the imaging property that model selection aims to improve.