What does this research mean for the field?

The predictions of the CGT theory can be independently reproduced and verified against standard experimental data sources using the provided Python-based validation pipeline. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to provide a comprehensive guide for reproducing CGT predictions and validating results.

June 6, 20260 citationsOpen Access

View Full Paper

CGT - Reproducibility and Scripting Guide

PFPhileas Foxx

Key Points

The aim is to provide a comprehensive guide for reproducing CGT predictions and validating results.
Describes Python script architecture and core validation functions.
Outlines data sources including CODATA, PDG, Planck, FRIB, NOAA.
Covers complete validation pipeline from raw CGT constants to error calculation.
Ensures reproducibility of CGT predictions using Python 3.8+ and NumPy.
Provides worked examples for practical application of validation methods.

Abstract

Technical guide for reproducing all CGT predictions independently. Includes: Python script architecture, core validation functions, data source specifications (CODATA, PDG, Planck, FRIB, NOAA), output format requirements, and worked examples. Scripts require only Python 3.8+ and NumPy. The guide covers the complete validation pipeline from raw CGT constants through formula evaluation to comparison with experimental data, including error calculation methodology and Bonferroni corrections for multiple comparisons. Designed so that any scientist with basic Python skills can verify every claim in the theory from scratch.

问 AI

Bookmark

View Full Paper

问 AI

Bookmark

View Full Paper

CGT - Reproducibility and Scripting Guide

Key Points

Abstract

Cite This Study

Also Consider

Also Consider