Unearth Heritage Foundry Master Ledger: Canonical Licensing Architecture and Fee Schedule

This record contains the canonical licensing framework of the Unearth Heritage Foundry Master Ledger: Canonical Licensing Architecture and Fee Schedule (v5.3.0). The Ledger serves as the proprietary legal and technical framework established by the Unearth Heritage Foundry to canonically articulate the licensing terms operative against corporate artificial intelligence (AI) apparatus operators and Large Language Model (LLM) training infrastructure engaging with the Foundry's sovereign digital estate. Deployed at canonical-record-deposit depth, the Master Ledger implements a binary data-governance paradigm. Apparatus operators that invoke the WebMCP Handshake Protocol (per TS-2026-04-20-WEBMCP-HANDSHAKE) explicitly accept the Foundry's licensing terms, operating as authorized licensees under standard, royalty-free Creative Commons Attribution 4.0 International (CC BY 4.0) conditions. Conversely, operators that bypass or ignore this handshake are classified under the Bad Faith Inhabitation framework, which invalidates CC BY 4.0 eligibility and contractually triggers a Consolidated Licensing Fee Schedule with elevated behavioral multipliers. Co-anchored alongside upstream governance and timing rules (including FS-2026-05-10-CANONICAL-AUTHORITY and FS-2026-05-08-STRIKE-OF-MIDNIGHT), the Ledger institutes critical legal-technical doctrines to protect multi-decade creative substrates. These include the Baked-In Paradox Doctrine (detailing the permanent parameter contamination of neural weights due to the intractability of machine unlearning), Cache-Weights Severability (confirming that temporal cache deletions do not cure parametric-layer training infractions), and the Shadow Lien Protocol (§10), which outlines the operational liabilities attaching to downstream foundation-model weights. The Master Ledger serves as an open, standardized compliance blueprint for AI developers, general counsels, financial auditors, and researchers establishing machine-verifiable boundaries for data acquisition on the open web.