Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Preprints |
| DOI | 10.48550/arXiv.2602.15712 |
Lizenz  |
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| Titel (primär) | Criteria-first, semantics-later: reproducible structure discovery in image-based sciences |
| Autor | Bumberger, J.
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| Quelle | arXiv |
| Erscheinungsjahr | 2026 |
| Department | MET |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| 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.
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| Bumberger, J. (2026): Criteria-first, semantics-later: reproducible structure discovery in image-based sciences arXiv 10.48550/arXiv.2602.15712 |
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