EmpiricalReadinessCert
plain-language theorem explainer
This structure aggregates five readiness predicates into a single certificate for Option A empirical work. Researchers preparing falsification tests or pipeline records in Recognition Science would reference it to confirm that all implemented combinations, the first-pass schedule, and the three named tensors satisfy the four-layer operational requirements. The declaration is a direct structure definition that packages the component predicates without further reduction steps.
Claim. An empirical readiness certificate consists of: a proof that every implemented combination $c$ satisfies the predicate of having a falsifiable protocol, an analysis action, a deliverable artifact, and a pipeline record; a proof that every combination in the first-pass schedule satisfies the same predicate; and explicit proofs that the oncology tensor, Miller span, and attention tensor combinations each satisfy the predicate.
background
The module defines empirical readiness for a combination $c$ as the conjunction ProtocolFalsifiable $c$ ∧ HasAnalysisAction $c$ ∧ HasDeliverable $c$ ∧ HasPipeline $c$. AllImplementedReady lifts this to a universal quantifier over all implemented combinations C1-C9. FirstPassReady restricts the same predicate to combinations appearing in the first-pass schedule. The local setting is a readiness gate that requires all four operational layers before empirical work proceeds, with zero sorries or axioms in the module.
proof idea
The declaration is a pure structure definition. It directly references the sibling definitions AllImplementedReady, FirstPassReady, and the three instances of EmpiricallyReady for the named combinations, with no tactics, lemmas, or reductions applied.
why it matters
The structure supplies the type for the downstream concrete certificate empiricalReadinessCert, which populates the fields from allImplementedReady, firstPassReady, and the three specific readiness theorems. It formalizes the readiness gate stated in the module documentation and supports preparation of empirical tests within the Recognition Science framework without adding new hypotheses.
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