RCFT/ULRC Protocol and Data Backlog (2022–2025): Regime Assignment, Audit Trail, Calibration History, and Multi-Domain Collapse Diagnostics
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This document provides the complete protocol, audit log, and multi-domain application data for the RCFT/ULRC system (Recursive Collapse Field Theory / Unified Language of Recursive Collapse). It includes canonical definitions, symbolic invariants, drift–fidelity–entropy–curvature equations, and regime classification logic used to assign system states into Stable, Watch, Collapse, or Critical conditions. The dataset spans 2022–2025 and is designed for scientific publication, regulatory audit, and algorithmic implementation. Key contents: Full invariant definitions and thresholds Cross-domain examples: environmental (EPA RadNet), biomedical (EEG, MIT-BIH), climate (NASA GISTEMP), power grid Synthetic edge case tests (low-variance, noise, periodicity, outlier injection) Handling of missing data (NaNs, gaps, hot restarts) False positive/false negative audit review Attribution logic for distinguishing spike vs. turbulence vs. hybrid collapse Multi-variable analysis (e.g., concordance/discrepancy across channels) Regime distribution histograms and non-event audit windows Parameter update/calibration log with rationale per change This file serves as the definitive supplement and empirical diagnostic reference for all RCFT/ULRC implementations through mid-2025. It is suitable for use in scientific journals, methods papers, compliance reports, and symbolic collapse engine deployment. Cite this document using the DOI provided. For questions or integration support, contact the author. Keywords: RCFT, ULRC, symbolic collapse, audit log, protocol, regime classification, entropy, drift, fidelity, climate anomaly, EEG, power grid, data diagnostics, reproducibility, regulatory compliance, calibration
本文件提供了RCFT/ULRC系统（递归坍缩场理论（Recursive Collapse Field Theory）/递归坍缩统一语言（Unified Language of Recursive Collapse））的完整协议、审计日志及多领域应用数据集。 本文件包含规范定义、符号不变量、漂移-保真度-熵-曲率方程，以及用于将系统状态划分为稳定、监视、坍缩或临界四类的状态分类逻辑。 本数据集时间跨度为2022年至2025年，旨在用于学术发表、监管审计及算法落地实现。 核心内容： 完整不变量定义与阈值 跨领域示例：环境领域（EPA RadNet）、生物医学领域（脑电图（EEG）、MIT-BIH数据库）、气候领域（NASA GISTEMP）、电网领域 合成极端场景测试（含低方差、噪声、周期性、异常值注入场景） 缺失数据处理方案（含缺失值（NaNs）、数据间隙、热重启场景） 假阳性/假阴性审计复盘 用于区分尖峰扰动、湍流坍缩与混合坍缩的归因逻辑 多变量分析（例如跨通道一致性/差异性分析） 状态分布直方图与非事件审计窗口 带每次变更依据的参数更新与校准日志 本文件可作为2025年中期前所有RCFT/ULRC落地应用的权威补充资料与实证诊断参考，适用于学术期刊投稿、方法学论文撰写、合规报告编制及符号坍缩引擎部署场景。 请使用本文提供的DOI进行引用。如有疑问或集成支持需求，请联系本文作者。 关键词：RCFT、ULRC、符号坍缩、审计日志、协议、状态分类、熵、漂移、保真度、气候异常、脑电图（EEG）、电网、数据诊断、可复现性、监管合规、校准
