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March 30, 20260 citationsOpen Access

A Novel Bridge Route from a Likelihood-Weighted Early-Universe Informational Structure to a Tiered High-Redshift Predictive Program

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AAAmmar Nasir Hussein al-Mantafji

Key Points

  • The aim is to develop a bridge framework connecting early-universe structures to high-redshift predictive models.
  • Created a bridge framework utilizing an observationally weighted early-universe structure.
  • Derived a bridge quantity based on the closure scale of the model.
  • Used multiple AI systems as tools for editorial and computational assistance.
  • Presents a tiered high-redshift predictive program with candidate branches.
  • Introduces a reproducible and externally testable bridge program.

Abstract

This record contains the complete research package for the paper A Novel Bridge Route from a Likelihood-Weighted Early-Universe Informational Structure to a Tiered High-Redshift Predictive Program. The work develops a bridge framework linking an observationally weighted early-universe informational structure to a falsifiable high-redshift predictive program through a derived bridge quantity based on the closure scale of the model. The package includes: the main manuscript PDF the LaTeX source figures tables a reconstruction pipeline a source manifest supporting documents The paper advances a disciplined claim: it presents a novel bridge route with a tiered high-redshift predictive program, including conservative, internally constrained, and JWST-calibrated candidate branches. The paper does not claim a final proof of a theory of everything and does not claim a complete first-principles derivation of the observed high-redshift mass function. Its goal is to provide a reproducible and externally testable bridge program. Multiple AI systems were used as editorial and computational assistance tools during the preparation of this research package. Final scientific responsibility, interpretation, selection, and approval remain with the author.

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Ammar Nasir Hussein al-Mantafji (2026) studied this question.

synapsesocial.com/papers/69c9c57ff8fdd13afe0bd718https://doi.org/10.5281/zenodo.19282828
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