Criteria-first, semantics-later: reproducible structure discovery in image-based sciences
This work addresses the vulnerability of existing image analysis methods to label drift under semantic-priority paradigms, which compromises their reliability in open science, cross-sensor/cross-site comparability, and long-term monitoring. To overcome this limitation, the authors propose a novel “standard-first, semantics-later” paradigm that decouples structural discovery from semantic mapping. By leveraging cybernetics, the principle that observation entails distinction, and information theory, they formulate explicit optimization criteria to extract stable, semantics-agnostic structures—such as partitions, structural fields, or hierarchies—prior to aligning them with domain-specific ontologies. This framework ensures that structural outputs remain independent of labeling schemes, enabling multiple interpretations and long-term interoperability. Validated across diverse domains, the approach demonstrates broad applicability in scenarios where labels are non-scalable, thereby advancing structural findings as FAIR, AI-ready digital objects suitable for digital twins and continuous monitoring.