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| Format: | Recurso digital |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20016725 |
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| _version_ | 1866901742931673088 |
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| author | Jeffrey, Daniel |
| author_facet | Jeffrey, Daniel |
| contents | <p><span>This archive contains the complete deterministic reproducibility package for:</span></p> <p><span>Is the S₈ Tension Structural? A Provenance-Based Reanalysis of Cross-Survey Covariance (Paper S).</span></p> <p> </p> <p><span>It provides all data, code, correlation-matrix construction logic, residual systematic implementation, and validation procedures required to regenerate every numerical result reported in the paper.</span></p> <p> </p> <p><span>The archive implements a provenance-encoded correlation framework across 36 published S₈ measurements spanning CMB, weak lensing, 3×2pt, RSD, cluster, and joint analyses. Residual systematic corrections are applied using literature-supported ranges without parameter fitting. </span></p> <p> </p> <p><span>All computations are executed through a deterministic pipeline with golden-output validation (tolerance = 0.0), SHA-256 checksum manifests, and ROOT_HASH chain-of-custody enforcement.</span></p> <p> </p> <p><span>All numerical values reported in Paper S correspond exactly to the outputs generated by this archived version 1.0.0 pipeline.</span></p> <p> </p> <p><span>This repository contains the full reproducibility package accompanying Paper S. The archive provides a deterministic implementation of the provenance-based correlation framework and residual systematic model used to evaluate cross-survey structural dependencies in published S₈ measurements.</span></p> <p> </p> <p><span>Archive contents</span></p> <p> </p> <p><span>Data</span></p> <p> </p> <p><span>• 36-measurement S₈ dataset<br>• conservative and upper-bound residual-corrected datasets<br>• provenance-encoded 36×36 correlation matrix<br>• synthesis and sensitivity-analysis outputs</span></p> <p> </p> <p><span>Code</span></p> <p> </p> <p><span>• correlation-matrix construction<br>• residual systematic application<br>• inverse-covariance synthesis<br>• sensitivity sweeps over correlation parameters<br>• consolidated metric export and validation</span></p> <p> </p> <p><span>Validation</span></p> <p> </p> <p><span>• golden-output files (tolerance = 0.0)<br>• schema and unit validation<br>• SHA-256 checksum manifest<br>• ROOT_HASH integrity file<br>• continuous-integration workflow</span></p> <p> </p> <p><span>Documentation</span></p> <p> </p> <p><span>• methodology overview<br>• provenance notes<br>• eigenvalue diagnostics<br>• robustness tests<br>• replication protocol and runbook</span></p> <p> </p> <p><span>Deterministic regeneration</span></p> <p> </p> <p><span>From the archive root:</span></p> <p> </p> <p><span>python code/minimal_run.py</span></p> <p><span>python code/validate_metrics.py</span></p> <p><span>python validation/rebuild_checksums.py</span></p> <p> </p> <p><span>Successful execution with no errors confirms full deterministic reproduction of the canonical archived results.</span></p> <p> </p> <p><span>Structural context</span></p> <p> </p> <p><span>This archive applies the deterministic reproducibility and covariance-governance framework defined in Paper 0 and implements the cross-probe correlation synthesis methodology used in Paper 1 within the late-time structure-growth domain.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20016725 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Precision Cascade Reproducibility Archive for "Testing Structural Independence Assumptions in S₈ Measurements" Jeffrey, Daniel <p><span>This archive contains the complete deterministic reproducibility package for:</span></p> <p><span>Is the S₈ Tension Structural? A Provenance-Based Reanalysis of Cross-Survey Covariance (Paper S).</span></p> <p> </p> <p><span>It provides all data, code, correlation-matrix construction logic, residual systematic implementation, and validation procedures required to regenerate every numerical result reported in the paper.</span></p> <p> </p> <p><span>The archive implements a provenance-encoded correlation framework across 36 published S₈ measurements spanning CMB, weak lensing, 3×2pt, RSD, cluster, and joint analyses. Residual systematic corrections are applied using literature-supported ranges without parameter fitting. </span></p> <p> </p> <p><span>All computations are executed through a deterministic pipeline with golden-output validation (tolerance = 0.0), SHA-256 checksum manifests, and ROOT_HASH chain-of-custody enforcement.</span></p> <p> </p> <p><span>All numerical values reported in Paper S correspond exactly to the outputs generated by this archived version 1.0.0 pipeline.</span></p> <p> </p> <p><span>This repository contains the full reproducibility package accompanying Paper S. The archive provides a deterministic implementation of the provenance-based correlation framework and residual systematic model used to evaluate cross-survey structural dependencies in published S₈ measurements.</span></p> <p> </p> <p><span>Archive contents</span></p> <p> </p> <p><span>Data</span></p> <p> </p> <p><span>• 36-measurement S₈ dataset<br>• conservative and upper-bound residual-corrected datasets<br>• provenance-encoded 36×36 correlation matrix<br>• synthesis and sensitivity-analysis outputs</span></p> <p> </p> <p><span>Code</span></p> <p> </p> <p><span>• correlation-matrix construction<br>• residual systematic application<br>• inverse-covariance synthesis<br>• sensitivity sweeps over correlation parameters<br>• consolidated metric export and validation</span></p> <p> </p> <p><span>Validation</span></p> <p> </p> <p><span>• golden-output files (tolerance = 0.0)<br>• schema and unit validation<br>• SHA-256 checksum manifest<br>• ROOT_HASH integrity file<br>• continuous-integration workflow</span></p> <p> </p> <p><span>Documentation</span></p> <p> </p> <p><span>• methodology overview<br>• provenance notes<br>• eigenvalue diagnostics<br>• robustness tests<br>• replication protocol and runbook</span></p> <p> </p> <p><span>Deterministic regeneration</span></p> <p> </p> <p><span>From the archive root:</span></p> <p> </p> <p><span>python code/minimal_run.py</span></p> <p><span>python code/validate_metrics.py</span></p> <p><span>python validation/rebuild_checksums.py</span></p> <p> </p> <p><span>Successful execution with no errors confirms full deterministic reproduction of the canonical archived results.</span></p> <p> </p> <p><span>Structural context</span></p> <p> </p> <p><span>This archive applies the deterministic reproducibility and covariance-governance framework defined in Paper 0 and implements the cross-probe correlation synthesis methodology used in Paper 1 within the late-time structure-growth domain.</span></p> |
| title | Precision Cascade Reproducibility Archive for "Testing Structural Independence Assumptions in S₈ Measurements" |
| url | https://doi.org/10.5281/zenodo.20016725 |