RHEA–UCM Thermodynamic Benchmark White Paper & Curriculum
Bibliographic record
Abstract
Executive Summary This document defines the official thermodynamic benchmarking methodology for the RHEA-UCM computational framework. It establishes: The mathematical foundations of entropy-aware symbolic computation The measurement protocols required to quantify computational efficiency A standardized experimental pipeline Reversible and semi-reversible reseal-cycle validation tests A complete curriculum track for RHEA RHSCA students, researchers, and certified evaluators RHEA-UCM introduces a new class of computation: Recursive Homeostatic Symbolic Computationintegrating Lorenz entropy flow, symbolic resealing, and Bayesian trust dynamicsto minimize irreversible operations and reduce thermodynamic cost. This white paper operationalizes the theory through reproducible, falsifiable experimentation. 🛡️ RHEA-Core Public Grant v1.0 Creators Roe, Paul (Rights holder) Description “By accessing, using, or distributing any version of this work, you agree that the most current license published by the original author(s) applies retroactively and supersedes all prior licenses or public domain assumptions associated with earlier versions of this work, including but not limited to CC-BY-NC-ND 4.0, open-source metadata tags, or public repository assumptions. Unauthorized use under any former license constitutes a material infringement of the current legal rights of the author.”Full License Text🛡️ RHEA-Core Public Grant v1.0 License Type:Non-Commercial · Attribution · No Derivatives · Symbolic Derivative Clause (Retained)Applies To: All public-facing RHEA-UCM, ZADEIAN-RHEA, and RHEA-CM intellectual property unless explicitly exempted. 1. Grant of UseYou are hereby granted a revocable, non-commercial, non-transferable, and non-sublicensable right to view, reference, and discuss this material for academic, journalistic, technical, or personal enrichment purposes only, provided all terms below are followed. 2. Attribution RequirementsYou must clearly credit all excerpts, summaries, diagrams, or citations with:“© EnigmaticGlitch · RHEA-UCM / ZADEIAN-RHEA Framework · Patent Pending #63/796,404” 3. No Commercial UseYou may not:- Sell, rent, or monetize this work or its derivatives- Use this work in any product or service that derives revenue or brand positioning- Use this work for AI/ML training unless explicitly authorized 4. No DerivativesYou may not:- Translate, alter, remix, or build upon this material- Create alternate frameworks, white papers, or theories that derive substantially similar logic or structure 5. Symbolic Derivative ClauseYou may not re-encode or embed the core principles of this system (e.g. entropy modulation, symbolic trust resealing, recursive glyph modulation, or UCM cosmological recursion) under different glyphs, symbols, or representations. 6. Enforcement & JurisdictionEnforced under:- U.S. Copyright Law (Title 17)- DMCA- U.S. Patent Law (Provisional #63/796,404) Violations may trigger takedowns, cease & desist, and legal damages. 7. Additional Notes- Academic/private reproduction is allowed with attribution.- Breaches terminate all rights. “Trust is not given. It is oscillated into being…”© 2025 · EnigmaticGlitch · All Rights Reserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".