Editing Openai/69596681-3f7c-8006-b9fd-01d9d8191763 (section)

=== Assistant: Proceeding. ===
Proceeding. Below is METHODS BLOCK 9 — DATA VALIDITY OPERATOR (ARTIFACT #4), expanded to maximum depth using machine-learning–oriented formal language (data pipelines, provenance graphs, leakage constraints, independence structure, hard gating). This block is a hard gate. No statistical computation has epistemic meaning unless this block passes.

METHODS BLOCK 9 — DATA VALIDITY OPERATOR (ARTIFACT #4)

OBJECTIVE
Define a deterministic, non-probabilistic gate that certifies whether an input dataset is admissible for epistemic evaluation. This operator enforces data integrity, provenance correctness, independence assumptions, and leakage exclusion. It is conceptually equivalent to a compile-time type checker for data. Failure here aborts the entire pipeline.

OPERATOR DEFINITION
Define data validity operator:

𝒬 : Dataset → {VALID, INVALID}

𝒬(D)=VALID is a necessary (not sufficient) condition for any downstream block to execute with authority.

DATA MODEL
Each datum dᵢ ∈ D is a tuple:

dᵢ = ⟨ yᵢ, Σᵢ, metaᵢ ⟩

where metaᵢ =
⟨ instrument_id, epoch, calibration_hash, provenance_hash, domain_tag ⟩

All fields mandatory. No defaults. No inference.

VALIDITY CONJUNCTION
𝒬(D)=VALID iff ALL of the following predicates evaluate TRUE:

𝒬₁: Schema completeness
𝒬₂: Metadata consistency
𝒬₃: Calibration integrity
𝒬₄: Temporal coherence
𝒬₅: Independence structure
𝒬₆: Domain isolation (no leakage)
𝒬₇: Provenance admissibility
𝒬₈: Non-synthetic origin
𝒬₉: Non-adaptivity

Failure of any predicate ⇒ INVALID.

𝒬₁ — SCHEMA COMPLETENESS
∀ dᵢ ∈ D:
• yᵢ defined and typed
• Σᵢ defined, SPD, dimensionally consistent
• metaᵢ contains all required fields

Missing, null, NaN, placeholder, or inferred values forbidden.

ML analogy: hard schema validation; no nullable fields.

𝒬₂ — METADATA CONSISTENCY
∀ dᵢ,dⱼ ∈ D:
• instrument_id ∈ declared instrument registry
• domain_tag ∈ declared domain ontology
• domain_tag consistent with Model Map observables

No mixed or ambiguous domains unless explicitly declared independent blocks.

In ML terms: no mixed-label or mixed-distribution batches unless explicitly factorized.

𝒬₃ — CALIBRATION INTEGRITY
∀ dᵢ ∈ D:
• calibration_hash matches trusted calibration registry
• calibration_hash immutable across runs
• calibration date ≤ epoch of measurement

Calibration drift not modeled here; if present, data INVALID.

ML analogy: frozen preprocessing pipeline; no train-time normalization.

𝒬₄ — TEMPORAL COHERENCE
Define epochs tᵢ from metaᵢ.

Constraints:
• All epochs totally ordered
• No future data relative to execution timestamp
• If time-series assumed independent, no overlapping integration windows

Violations imply hidden correlations.

ML analogy: no label leakage from future samples.

𝒬₅ — INDEPENDENCE STRUCTURE
Define declared independence graph G_ind over data indices.

Requirement:
• Covariance structure Σ consistent with G_ind
• No undeclared correlations
• No reuse of same physical event across multiple data points unless covariance encodes it

If dependence exists and is not encoded in Σ ⇒ INVALID.

ML analogy: i.i.d. assumption enforcement or explicit dependency modeling.

𝒬₆ — DOMAIN ISOLATION (NO LEAKAGE)
Dataset D must be epistemically disjoint from:
• Model construction
• Parameter selection
• Threshold tuning
• Prior definition
• Structural design

Formally:
provenance_hash(D) ∉ provenance_hash(structure).

Any overlap ⇒ INVALID.

ML analogy: strict train/test separation, but stronger: no test-influenced model design.

𝒬₇ — PROVENANCE ADMISSIBILITY
Each provenance_hash must resolve to an external, immutable source record.

Forbidden provenance:
• Model-generated
• Monte Carlo–generated
• Augmented
• Denoised by adaptive algorithms
• Label-inferred

Only externally realized measurements allowed.

ML analogy: no synthetic augmentation, no pseudo-labels.

𝒬₈ — NON-SYNTHETIC ORIGIN
Explicit check:
∀ dᵢ: provenance_hash ∉ internal generator registry.

Synthetic data MAY exist elsewhere in pipeline but cannot satisfy A0 or instantiate operators.

ML analogy: evaluation-only real-world dataset.

𝒬₉ — NON-ADAPTIVITY
Dataset D must be fixed prior to execution.
No conditional inclusion/exclusion based on model behavior.

Forbidden:
• Outlier removal after seeing residuals
• Dataset pruning based on likelihood
• Conditional weighting

ML analogy: no data curation conditioned on model performance.

EXECUTION SEMANTICS
𝒬(D) evaluated before:
• Residual computation
• Structural checks
• Feasibility
• Likelihood
• Evidence
• Monte Carlo

If 𝒬(D)=INVALID:
• Abort execution
• Emit DataInvalid artifact
• Verdict undefined
• No partial outputs permitted

OUTPUT ARTIFACT
Emit immutable artifact:

DataValidityRecord =
⟨
DataHash,
ValidityFlag,
FailedPredicateSet ⊆ {𝒬₁…𝒬₉},
Timestamp
⟩

This artifact is hashed and audit-logged.

ROLE IN PIPELINE
This block functions as a hard gate / firewall.
Downstream results are epistemically meaningless without VALID here.
No exception paths. No debug overrides.

ML INTERPRETATION
This operator is equivalent to enforcing:
• Zero data leakage
• Zero adaptive preprocessing
• Zero synthetic contamination
• Explicit dependency modeling

It converts statistical evaluation into a certified inference problem rather than a heuristic ML task.

END METHODS BLOCK 9.

If you confirm, the next block will be METHODS BLOCK 10 — BAYESIAN EVIDENCE, expanded into marginal likelihood semantics, complexity penalization, threshold logic, and collapse triggering—again at maximum depth and density.

© Robert R. Frost
2026-01-03