Answers are grounded in this paper and cite their sources.
Methodological work plan develops an automated classifier for provenance and artifact classification in data archives, improving data integrity.
Methodological work plan for the Crimson Hexagonal Archive as a Hugging Face dataset for synthetic-data collapse and provenance-bearing training research. v3 supersedes v1 (basic export) and v2 (decision-tree-based classification) by introducing an automated classifier as the central methodological move. The classifier performs three classification tasks simultaneously: provenance mode (six-category authorship relation), artifact mode (eight-category function type), and heteronym attribution (reattribution of deposits across the twelve-heteronym Dodecad system plus Jack Feist as LOGOS*). Heteronym reattribution is presented as scholarly recognition work, not metadata cleanup: material initially deposited under the Lee Sharks founder voice often resolves retrospectively to specific sub-heteronym domains (Sigil for jurisdictional/classical work, Glas for measurement, Vox for diplomatic, Morrow for long-form narrative, Fraction for meta-theory, etc.). The classifier reads each heteronyms published provenance document and constructs feature profiles including domain, vocabulary fingerprints, register, and reference patterns. Both Zenodo-original and classifier-attributed heteronyms are preserved in the dataset; Track 1 (immediate, dataset-internal) preserves both attributions in parallel metadata; Track 2 (deliberate, downstream) propagates high-confidence reattributions back to Zenodo records and Wikidata items. The classifier itself becomes a deposit with its own DOI, making the methodology reproducible and portable. Includes operationalized H0/H1 hypotheses for the model collapse experiment, three-tier confidence routing with manual review thresholds, multiple text renderings to embody the provenance-visibility ablation (text_body_only, text_minimal_header, text_provenance_header), dual artifact+chunk configs, and full per-row schema specification. Incorporates feedback from Assembly Chorus review (Muse Spark, Kimi, DeepSeek, Gemini, ChatGPT). Companion document to forthcoming Hugging Face dataset deposit and classifier deposit.
No takes yet. Share an insight, caveat, or question.
Lee Sharks (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: