Each post is a discovery. IRDME is the method — the finding is the story. All results link to their pre-registered hash.
Across 146 cross-layer analyses in 33 pre-registered experiments spanning 8 domains, we found two structural classes that appear in every domain: Universal Hubs (same node tops multiple layers) and Layer Specialists (different nodes lead each layer). Universal hubs are the infrastructure: TP53, transformer, W boson, VFS, app, thalamus. Layer specialists are the disruptors: LLaMA vs transformer, CHEK1 vs TP53, IRQ vs VFS, A[15] vs A[0]. Both classes are detectable from graph topology without reading any domain content. A third class, Circular Hubs, appears specifically in pharmaceutical evidence chains (monoamine_hypothesis, pain_undertreated_claim).
We reanalyzed all 45 cross-layer hub correlation results across 33 pre-registered IRDME experiments to compare Spearman rho and Pearson r. Overall: Pearson is marginally higher on average (0.510 vs 0.463). But the GAP between them is not noise -- it is a structural diagnostic. Large positive gaps (Spearman >> Pearson) appear consistently in cross-species and cross-domain settings where rank ordering is preserved but magnitudes differ. Large negative gaps (Pearson >> Spearman) appear in within-domain settings where absolute hub centrality scores carry real structural signal. The gap tells you what kind of cross-layer relationship you are measuring.
How to use one experiment's hub output to seed a comparative analysis. Introduces the --seed-from and --compare-to flags, the role-vector cosine metric, and an exploratory C. elegans generative compression result (avg_seed_cosine=0.845, avg_rank_cosine=0.888). Not a confirmatory pre-registered result - a methodological contribution.