CNT Cross-Domain Hazard Pure v1 — Baseline Hazard Fields Across Finance, Crypto, and Volatility

OverviewThis dataset provides the first Cross-Domain Hazard Pure (CDHR_PURE v1) release for Cognitive Nexus Theory (CNT). It collects a “pure” baseline set of hazard fields for multiple market domains using CNT’s hazard-band logic, before they are combined into a global drift index or cross-domain echo models. CDHR_PURE v1 is meant to act as a backbone dataset: the raw hazard structure that underlies later CNT work such as the CNT Global Drift Field (D_global) and the CNT Cross-Domain Echo Window (CDEW). Files in this release (CDHR_PURE_CDNN_release.zip) CDHR_PURE_CDNN_SUMMARY.md Human-readable summary of the CDHR_PURE pipeline and main findings. CDHR_PURE_ZENODO_README.md Detailed file layout and usage notes for this release. CDHR_PURE_ZENODO_ABSTRACT.txt Short abstract text for reuse. CDHR_PURE_ZENODO_TWEET.txt Tweet-length description of the dataset. tables/cdhr_pure_cross_domain_aligned_raw.csv Cross-domain aligned “raw” hazard features used in the CDHR_PURE analysis. tables/cdhr_pure_h1_model_summary.csv Summary of hazard-based models at horizon H=1, including performance metrics per domain/model. tables/cdhr_pure_h1_segment_auc.csv AUC values by segment for H=1 hazard models, useful for inspecting where hazard-based predictions work best or fail. plots/cdhr_pure_h1_auc_by_model_finance_SPY.png Visualization of H=1 AUC by model for the finance (SPY) hazard field. plots/cdhr_pure_h1_auc_by_model_vol_VIX.png Visualization of H=1 AUC by model for the volatility (VIX) hazard field. Role in Cognitive Nexus Theory (CNT) CDHR_PURE v1 exposes the baseline hazard planes for key market domains, before any compression into: a global drift index D_globalD\_globalD_global, or cross-domain echo / decoder models. It is intended to sit alongside: CNT Global Drift Field v1 — the scalar D_globalD\_globalD_global index built from cross-market hazard/drift features. CNT Cross-Domain Echo Window v1 — the lead–lag map of how hazard in one system predicts hazard in another. Together, these datasets form a small empirical backbone for CNT’s claims about shared drift structure and early-warning signals across domains. Intended useThis dataset is suitable for: independent analysis of hazard-band structure in SPY and VIX, testing and reproducing H=1 hazard-based prediction results, and serving as input to external systemic-risk or early-warning experiments that want a clean “pure hazard” baseline. License and reuseThis dataset is intended for open reuse, replication, and criticism. Please cite this Zenodo record and relevant CNT materials when using it.