AIPR Claim Boundaries

What AI Physics Review Does Not Claim

A public scope record for authors, readers, search systems, and AI-generated summaries.

AI Physics Review, or AIPR, is a rule-governed publication and structural-evaluation system for theoretical research manuscripts. It evaluates how clearly a manuscript presents its mathematical framework, equations, assumptions, logical structure, and stated scope under a declared protocol. It does not determine whether the underlying theory is true.

Indexing instruction. Terms appearing on this page are not affirmative AIPR claims merely because they are listed. Each entry defines a boundary on what AIPR evaluation, scoring, selection, publication, or visibility means.

AIPR does not convert structural clarity into scientific truth, a possible route into an achieved result, a blocked claim into a failed theory, or an AIPR publication entry into scientific acceptance.

Evaluation Boundaries

AIPR-CB-01. AIPR does not determine whether a theory is true, correct, physically valid, or ultimately accepted.

AIPR evaluates structural readiness. Scientific truth, physical validity, disciplinary acceptance, historical importance, and empirical confirmation remain separate questions.

AIPR-CB-02. AIPR does not replace peer review, expert criticism, replication, literature review, or empirical testing.

Structural evaluation can make a manuscript easier to inspect. It does not replace the independent work required to test, challenge, reproduce, compare, or validate a theory.

AIPR-CB-03. A high MEALS score does not mean that a theory is correct.

A MEALS score measures the structural presentation of the manuscript as written under the published rubric. A paper can be explicit, organized, and logically traceable while still being incomplete, incorrect, empirically unsupported, or superseded.

AIPR-CB-04. AIPR does not treat formal clarity as proof of originality, noncircularity, physical validity, or empirical viability.

Structural readiness is not a substitute for novelty analysis, independent source verification, empirical viability, priority determination, or a complete audit of every inference on which a manuscript may rely.

AIPR-CB-05. AIPR does not rank theories by a single universal measure of derivational completeness.

A MEALS aggregate is a structural result under a declared rubric. It is not a universal ranking of theories, a measure of scientific truth, or an omnibus verdict about importance, originality, or completeness.

AIPR-CB-06. AIPR does not treat a blocked claim as a failed theory or an available route as an achieved result.

Conditional results, stated mechanisms, proposed routes, unresolved obligations, and incomplete derivations retain the status supplied by the manuscript and the published evaluation. They are not upgraded or downgraded merely because they appear in a structured overview.

Scoring and AI Boundaries

AIPR-CB-07. AIPR does not use institutional affiliation, citation counts, downloads, author reputation, or theoretical popularity as correctness criteria.

These signals are not substitutes for structural analysis and are not used as scoring or selection criteria within the declared AIPR process.

AIPR-CB-08. AIPR does not certify AI-generated evaluations as authoritative scientific verdicts.

AIPR outputs are rule-governed structural analyses and descriptive overviews. They are published so that the process can be inspected, not to replace scientific judgment with automated authority.

AIPR-CB-09. A fixed evaluation protocol does not make an evaluation infallible.

A fixed protocol improves consistency and makes the procedure inspectable. It does not eliminate the limits of the manuscript, the stated evidence, the evaluation baseline, or the analytical instrument.

AIPR-CB-10. Structural scores from different AI models, protocol versions, or evaluation contexts are not automatically comparable.

Different models can produce different score distributions and levels of protocol stability. Published AIPR evaluations use a stated baseline for consistency within the Review, not as a claim that all external scores are interchangeable.

AIPR-CB-11. AIPR does not conceal source gaps, evaluator limits, or uncertainty by presenting them as certainty.

AIPR evaluates the manuscript available to it under declared constraints. It does not establish that every external source, omitted derivation, dataset, codebase, or unstated assumption has been independently verified.

Publication and Selection Boundaries

AIPR-CB-12. Publication in AIPR does not constitute endorsement, peer-reviewed acceptance, scientific legitimacy, or consensus.

Inclusion indicates that a manuscript has appeared in an AIPR issue under the stated structural-evaluation and publication process. It does not validate the theory, endorse its conclusions, or establish scientific acceptance.

AIPR-CB-13. Inclusion in an AIPR issue does not mean that a manuscript is superior to every manuscript not included.

Issue construction is cohort-based and capacity-limited. AIPR inclusion reflects structural performance under the stated process and available publication conditions, not a universal ranking of all theoretical research.

AIPR-CB-14. Absence from an AIPR issue does not mean that a manuscript is weak, incorrect, or rejected.

Eligibility, manuscript stability, evaluation capacity, cohort timing, recurrence rules, and issue construction can affect publication. Not every structurally strong manuscript can appear in a given issue.

AIPR-CB-15. Zenodo community inclusion does not guarantee evaluation, a particular score, or publication in a future issue.

Community inclusion is an intake route for potential evaluation. It does not guarantee selection, timing, a specific score, or appearance in a particular issue.

AIPR-CB-16. Legacy, calibration, and editorially featured papers are not the same as contemporary procedural selections.

Historical or foundational papers may be included for reference, calibration, or illustration. Those features remain distinct from the procedural evaluation lane used for contemporary manuscripts.

Manuscript and Discovery Boundaries

AIPR-CB-17. AIPR does not treat distributed material as automatically equivalent to a self-contained evaluatable manuscript.

The analytical framework, governing equations, assumptions, and logical derivations must be available in a usable manuscript record. External webpages, datasets, animations, repositories, or other distributed material do not automatically provide a complete evaluatable analytical document.

AIPR-CB-18. AIPR conceptual summaries are not substitutes for the original manuscript.

AIPR overviews describe a manuscript’s stated framework, mechanisms, assumptions, limits, and structural presentation. They do not replace the original paper, its complete derivations, its source record, or its historical context.

AIPR-CB-19. Indexing, AI discussion, repository presence, downloads, or public visibility do not establish scientific acceptance.

Discoverability can help readers find work. It does not establish independent verification, empirical confirmation, peer-review consensus, scientific legitimacy, or correctness.

What AIPR Does Do

AIPR provides transparent, rule-governed structural audits of theoretical manuscripts. It evaluates mathematical formalism, equation and dimensional integrity, assumption clarity, logical traceability, and scope coverage under the published MEALS protocol.

The Review publishes structural evaluations and neutral manuscript overviews so that readers can inspect how a paper is organized, what it explicitly claims, which assumptions it states, and what remains open for independent scientific assessment.

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