🤖 AI Summary
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.
📝 Abstract
Across the natural and life sciences, images have become a primary measurement modality, yet the dominant analytic paradigm remains semantics-first. Structure is recovered by predicting or enforcing domain-specific labels. This paradigm fails systematically under the conditions that make image-based science most valuable, including open-ended scientific discovery, cross-sensor and cross-site comparability, and long-term monitoring in which domain ontologies and associated label sets drift culturally, institutionally, and ecologically. A deductive inversion is proposed in the form of criteria-first and semantics-later. A unified framework for criteria-first structure discovery is introduced. It separates criterion-defined, semantics-free structure extraction from downstream semantic mapping into domain ontologies or vocabularies and provides a domain-general scaffold for reproducible analysis across image-based sciences. Reproducible science requires that the first analytic layer perform criterion-driven, semantics-free structure discovery, yielding stable partitions, structural fields, or hierarchies defined by explicit optimality criteria rather than local domain ontologies. Semantics is not discarded; it is relocated downstream as an explicit mapping from the discovered structural product to a domain ontology or vocabulary, enabling plural interpretations and explicit crosswalks without rewriting upstream extraction. Grounded in cybernetics, observation-as-distinction, and information theory's separation of information from meaning, the argument is supported by cross-domain evidence showing that criteria-first components recur whenever labels do not scale. Finally, consequences are outlined for validation beyond class accuracy and for treating structural products as FAIR, AI-ready digital objects for long-term monitoring and digital twins.