PRISMA CP364 — Complete Structural Analysis Dossier

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Authors: Claudio Pizzuti
Cited by: 0

Abstract

This dossier constitutes the second element of the PRISMA CP364 sequence and addresses the structural and methodological consolidation level of the analytical program. It is conceived as a complete, self-contained analytical corpus whose purpose is to document, in full detail, the methodological architecture, data provenance, metric definitions, robustness controls, and comparative procedures underlying the descriptive results established in the first dossier of the sequence. The present dossier does not introduce new observational results and does not revise or reinterpret previously established findings.Its role is to make explicit how those results were obtained, verified, and constrained, enabling independent audit, replication, and critical evaluation. Specifically, this dossier provides: full traceability of all observational data sources; a detailed description of the PRISMA CP364 pipeline architecture; explicit definitions of the structural metrics employed; normalization procedures and comparison criteria; robustness checks and anti-bias measures; complete presentation of results at the individual-object level; comparative analysis within the PRISMA structural signature space; explicit discussion of limits and scope of validity. All analyses are performed using publicly available data and a fixed, conservative, and non-adaptive pipeline, applied identically across all objects considered.No manual tuning, post-hoc filtering, or result-driven optimization is performed at any stage. The purpose of this dossier is not interpretative.It establishes a transparent, reproducible, and auditable foundation that supports the descriptive conclusions of the PRISMA CP364 program while deliberately refraining from causal or theoretical claims. Within the PRISMA CP364 sequence, this dossier serves as the structural consolidation layer, ensuring that the descriptive results rest on a fully documented and verifiable analytical framework. It provides the necessary conditions for informed independent assessment and forms the stable basis upon which subsequent, epistemically distinct dossiers may be situated, without modifying or extending the results already established.

Fields of Study & Subjects

Scientific Computing and Data Management Research Data Management Practices Machine Learning in Materials Science

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
8 January 2026
Language
IT
Document type
Preprint