Predictive Modeling Discipline for Bayyinah: Cross-Domain Adaptations from Organic Chemistry, Astrophysics, Software-Audit-Practice, and AI-Delegation Empirical Methodology

The Predictive Modeling Discipline (PMD) v1.8 is the operational-specification tier of the BayyinahEnterprise methodology portfolio. It specifies how predictive-modeling discipline from four source domains -- organic chemistry, astrophysics, software-audit-practice, and AI-delegation empirical methodology -- is imported into the Bayyinah ecosystem's auditing, predictive-programming, and applications work, including the Bayyinah Audit Framework, the furqan-lint static analyzer, the Furqan programming language, and the Bayyinah Integrity Scanner. The document is organized around three structural commitments: tier-graded imports (Tier 1 / Tier 2 / Tier 3 classification per §0.1); named self-attestation as structural constraint, empirically anchored to the DELEGATE-52 benchmark (Laban, Schnabel, Neville 2026) which measures approximately 25% content corruption in long delegated LLM workflows; and recursive application of the structural-honesty thesis to the methodology document itself, including the M1.5 append-only self-application discipline (§3A.10) and the M1.6 codification-cycle self-error empirical pattern (§3A.11). PMD v1.8 documents 52 cycles of append-only audit absorption. Contributions specific to v1.8 include: §3A.12.4 vendor-pool honesty (verdict-type differentiation between judgment-laden substrate-fidelity calls and mechanical reproducible checks); §3A.12.5 LaTeX-compile gate for math-bearing canon; §3B.6 operational-substrate-capacity constraint with adapted termination criterion; and consolidated §3A.13 watchlist additions (WL26 sibling-spec rendering, WL27 load-bearing modeling-assumption naming, WL28 sole-contact meta-claim in prose, WL29 Gemini-cluster element-enumeration non-binding). PMD v1.8 is the internal operational realization of the discipline stated in publicly portable form as Verify, Do Not Trust: A Universal Method for Structural Honesty Verification (SHV v1.1, DOI 10.5281/zenodo.21449063). It is deposited in advance of the dissertation Structural Honesty: A Mathematical Theory of Surface-Substrate Consistency Across Substrates and Time (Arfeen, targeted September 2026), which cites this methodology at operational loci, in conformance with the append-only provenance discipline formalized as the dissertation's Axiom 1.