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module module moderate

IndisputableMonolith.Verification.Measurement.DataProvenance

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Defines Lean types for empirical data sources, provenance records, measurement results, and hypothesis status used in Recognition Science measurement verification. Anyone checking RS predictions against lab or observational numbers cites these containers. The module is pure structure: inductive and structure definitions with raw constants and hypothesis wrappers, no theorems.

claimA small type layer for measurement audit: data-source tags, provenance records, measurement results, hypothesis status, and named raw thresholds ($C_{\mathrm{net}}$, $C_{\mathrm{proj}}$, $C_{\mathrm{eng}}$, classify and stability cuts) together with hypothesis interfaces that stand in for those cuts until discharged by data.

background

Recognition Science claims quantitative contact with experiment (mass ladder, coupling band, geometric thresholds). Verification therefore needs an explicit place to record where a number came from, what was measured, and whether a named cut is still a hypothesis or has been fixed by data.

This module sits under Verification.Measurement. It introduces source and provenance types, a measurement-result carrier, and a hypothesis-status flag, plus raw numeric placeholders for classify and stability thresholds and for network, projection, and energy cost constants. Sibling names indicate the intended split: concrete raw values versus *_hypothesis props that can later be replaced by proved bounds.

Only Mathlib is imported. No physics lemmas live here; the file is the bookkeeping front end for later comparison of RS-native predictions to external datasets.

proof idea

This is a definition module, no proofs. It declares inductive or structure types (data source, provenance, measurement result, hypothesis status, data hypothesis) and binds raw constants and hypothesis interfaces for thresholds and cost coefficients. Argument structure is deferred to consumers that inhabit or discharge those interfaces.

why it matters in Recognition Science

Without typed provenance, measurement claims in the monolith cannot be audited: every comparison of an RS formula to a number needs a source tag and a status (raw constant vs open hypothesis). Downstream verification developments that classify stability, net/projection/energy costs, or empirical cuts are expected to consume these carriers so that conditional results stay explicitly conditional.

In the broader framework this supports honest status reporting next to forced structure (T5–T8, RCL, phi-ladder masses, alpha band): geometry and cost identities can be proved in Lean, while lab thresholds remain data-backed hypotheses until replaced. The module itself proves nothing; it makes the data boundary visible.

scope and limits

declarations in this module (13)