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Precision Cascade Reproducibility Archive for "Covariance Completeness in the Distance Ladder"

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DataCite Commons2026-05-03 更新2026-05-07 收录
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https://zenodo.org/doi/10.5281/zenodo.20016484
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This archive provides a complete, deterministic, and self-auditing reproducibility framework for the manuscript:   “Resolving the Hubble Tension Through Systematic Correlation Analysis (Revised Version)”.   The archive implements the Precision Cascade Engine, a provenance-encoded, covariance-driven inference system designed to reproduce and audit the full statistical pathway from declared inputs to final cosmological estimates. All numerical results are generated internally from:   declared measurements structural provenance mappings covariance construction rules systematic bias models   The archive reproduces the primary result:H₀ = 69.9 ± 0.8 km s⁻¹ Mpc⁻¹,reducing the Hubble tension from ~5.9σ to 2.4–2.8σ through explicit inclusion of cross-rung covariance.   Key features:   Fully deterministic execution (no stochastic elements) Single self-contained archive (no external dependencies beyond environment setup) Explicit construction of correlation and covariance matrices from provenance structure Inverse-covariance estimator implementation Executable worked examples (2×2 and 3×3 covariance systems) Sensitivity analyses for correlation strength and systematic variation Internal validation against golden outputs Checksum closure with ROOT_HASH for chain-of-custody integrity Self-auditing framework with PASS/FAIL governance Explicit handling and disclosure of residual differences between illustrative and exact computational layers   The archive is designed to:   enable full independent replication support adversarial testing of assumptions provide a transparent audit trail for all reported results   This work represents a reproducibility-first implementation of cosmological inference, prioritising process integrity over narrative presentation.   Keywords   Hubble tension, H0, cosmology, covariance modelling, reproducibility, statistical inference, systematics, Bayesian inference, scientific computing, auditability, precision cascade
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Zenodo
创建时间:
2026-05-03
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