Measurement Validation Ontology

# AI Measurement Validation Ontology — Flow Algebra

URL: https://kirethresearch.org/booksai-core/ontologies/mathematical-ontologies/measurement-units-ontologies/ai-measurement-semantics/measurement-validation-ontology-2.html

FLOW AIMeasurementValidationOntology {

  DOMAIN:
    Mathematical Ontologies
    -> Measurement & Units Ontologies
    -> AI Measurement Semantics
    -> Measurement Validation Ontology

  PURPOSE:
    Represent measurement validation as a machine-interpretable semantic process through which an AI system evaluates whether a measurement record, result, dataset, instrument output, derived value or measurement model is internally coherent, dimensionally valid, contextually complete, metrologically supported, uncertainty-aware, provenance-linked and suitable for a declared use. The ontology combines semantic validation, unit and dimension checks, range and plausibility testing, uncertainty validation, calibration and traceability checks, provenance validation, temporal and spatial consistency, statistical validation, transformation-lineage validation, standards context, conflict detection, confidence management and recursive revalidation while preventing a syntactically valid numerical record from being mistaken for a valid measurement.

  ENTITY AIMeasurementValidation {
    TYPE: MachineInterpretableMeasurementAssessment

    PROPERTIES:
      validates_identity
      validates_measurand
      validates_quantity_kind
      validates_value
      validates_unit
      validates_dimension
      validates_uncertainty
      validates_calibration
      validates_traceability
      validates_context
      validates_provenance
      validates_lineage
      validates_statistics
      detects_conflicts
      assigns_validation_status
      supports_recursive_revalidation
  }

  ENTITY ValidationTarget {
    TYPE: MeasurementSemanticEntity

    POSSIBLE:
      raw_measurement
      measurement_result
      derived_measurement
      dataset
      instrument_output
      calibration_result
      uncertainty_budget
      measurement_model
      transformed_value
  }

  ENTITY ValidationRule {
    TYPE: MachineExecutableSemanticConstraint

    COMPONENTS:
      rule_identifier
      target_type
      condition
      evidence_requirement
      severity
      outcome
      provenance
  }

  ENTITY ValidationEvidence {
    TYPE: SupportingMeasurementInformation

    POSSIBLE:
      metadata
      raw_data
      calibration_certificate
      reference_standard
      certified_reference_material
      method
      standard
      uncertainty_budget
      provenance_record
      statistical_evidence
  }

  ENTITY ValidationResult {
    TYPE: ScopedAssessmentResult

    POSSIBLE:
      valid
      valid_with_warning
      invalid
      indeterminate
      incomplete
      requires_review

    RULE:
      validation_result MUST be scoped_to_target_rules_context_andintended_use
  }

  VALIDATION_PIPELINE_MODEL {
    stages:
      identity_validation
      structural_validation
      semantic_validation
      unit_validation
      dimensional_validation
      contextual_validation
      calibration_validation
      traceability_validation
      uncertainty_validation
      provenance_validation
      lineage_validation
      statistical_validation
      plausibility_validation
      standards_validation
      final_decision

    RULE:
      failure_at_one_stage SHOULD remain_traceable_to_specific_rule
  }

  IDENTITY_VALIDATION_MODEL {
    checks:
      measurement_identifier
      source_identifier
      entity_type
      namespace

    RULE:
      unresolved_identity MUST prevent_false_entity_merging
  }

  DUPLICATE_IDENTITY_MODEL {
    candidate_records:
      same_identifier_orapparently_same_event

    RULE:
      duplicates MUST be resolved_using_source_time_instrument_andprovenance
  }

  STRUCTURAL_VALIDATION_MODEL {
    checks:
      required_fields
      datatypes
      cardinality
      schema_version
      serialisation

    RULE:
      structural_validity != measurement_validity
  }

  REQUIRED_FIELD_MODEL {
    fields:
      depend_on_measurement_type

    RULE:
      missing_required_field MUST generate_explicit_validation_issue
  }

  DATATYPE_MODEL {
    expected:
      numeric
      string
      identifier
      timestamp
      vector
      matrix
      distribution

    RULE:
      correct_datatype_does_not_establish_semantic_correctness
  }

  MEASURAND_VALIDATION_MODEL {
    checks:
      measurand_identifier
      object_or system
      property
      conditions
      matrix
      temporal_context

    RULE:
      ambiguous_measurand MUST be flagged
  }

  QUANTITY_KIND_VALIDATION_MODEL {
    quantity_kind:
      canonical_semantic_type

    checks:
      unit_compatibility
      dimension
      method
      domain

    RULE:
      quantity_kind != dimension
  }

  NUMERICAL_VALUE_VALIDATION_MODEL {
    checks:
      parseable
      finite
      expected_shape
      sign
      range
      precision

    RULE:
      parseable_number != valid_measurement
  }

  FINITE_VALUE_MODEL {
    invalid_or special:
      NaN
      positive_infinity
      negative_infinity
      overflow
      underflow

    RULE:
      machine_numeric_state MUST be explicitly handled
  }

  UNIT_VALIDATION_MODEL {
    checks:
      unit_identifier
      symbol
      case
      prefix
      unit_system
      scale
      offset

    RULE:
      free_text_unit SHOULD be resolved_to_canonical_unit_before_reasoning
  }

  UNIT_SYMBOL_AMBIGUITY_MODEL {
    examples:
      m
      s
      T
      C
      N

    RULE:
      symbol MUST be resolved_using_quantity_anddomain_context
  }

  DIMENSIONAL_VALIDATION_MODEL {
    quantity_kind_dimension:
      D_q

    unit_dimension:
      D_u

    REQUIRE:
      D_q =
        D_u

    RULE:
      mismatch_is_semantic_validation_failure
  }

  SAME_DIMENSION_DIFFERENT_KIND_MODEL {
    examples:
      energy_vs_torque
      frequency_vs_activity
      absorbed_dose_vs_dose_equivalent

    RULE:
      dimension_match_does_not_complete_semantic_validation
  }

  COMPOUND_UNIT_VALIDATION_MODEL {
    unit_expression:
      parse_tree

    process:
      derive_dimension
      derive_scale
      validate_quantity_kind

    RULE:
      compound_unit MUST not_be validated_by_string_match_only
  }

  UNIT_CONVERSION_VALIDATION_MODEL {
    source:
      unit_A

    target:
      unit_B

    checks:
      compatible_quantity_kind
      compatible_dimension
      factor
      offset
      exactness
      context

    RULE:
      conversion MUST preserve_underlying_quantity
  }

  AFFINE_QUANTITY_MODEL {
    example:
      temperature_scale_values

    RULE:
      absolute_values_anddifferences MUST use_distinct_conversion_semantics
  }

  RANGE_VALIDATION_MODEL {
    value:
      x

    declared_range:
      [L,U]

    condition:
      L <= x <= U

    RULE:
      distinguish_instrument_range_method_range_calibrated_range_andphysical_range
  }

  PHYSICAL_BOUND_MODEL {
    possible:
      nonnegative
      bounded_fraction
      absolute_temperature
      probability
      concentration

    RULE:
      physical_bound MUST be quantity_specific
  }

  PLAUSIBILITY_MODEL {
    evidence:
      domain_knowledge
      historical_values
      physical_model
      peer_measurements

    RULE:
      implausibility_is_warning_orreview_trigger_not_automatic_falsehood
  }

  CONTEXT_COMPLETENESS_MODEL {
    possible_context:
      time
      location
      environment
      sample
      instrument_state
      method
      reference

    RULE:
      required_context_depends_on_measurand_andintended_use
  }

  TEMPORAL_VALIDATION_MODEL {
    checks:
      timestamp
      time_zone
      time_scale
      acquisition_order
      calibration_validity_time
      method_version_time

    RULE:
      impossible_or inconsistent_event_order MUST be flagged
  }

  SPATIAL_VALIDATION_MODEL {
    checks:
      coordinates
      coordinate_reference_system
      altitude
      depth
      location_role

    RULE:
      coordinates_without_reference_system MAY be ambiguous
  }

  ENVIRONMENT_VALIDATION_MODEL {
    checks:
      temperature
      humidity
      pressure
      vibration
      electromagnetic_conditions

    RULE:
      environmental_limit_exceedance MAY invalidate_orincrease_uncertainty
  }

  SAMPLE_VALIDATION_MODEL {
    checks:
      sample_identifier
      matrix
      preparation
      collection
      storage
      chain_of_custody

    RULE:
      sample_identity MUST remain_linked_to_result
  }

  INSTRUMENT_VALIDATION_MODEL {
    checks:
      instrument_identifier
      range
      resolution
      configuration
      firmware
      status
      calibration

    RULE:
      instrument_state MUST correspond_to_observation_time
  }

  SENSOR_VALIDATION_MODEL {
    checks:
      sensor_identifier
      calibration
      sensitivity
      range
      host_configuration

    RULE:
      sensor_replacement MUST not_inherit_previous_identity_without_evidence
  }

  CALIBRATION_VALIDATION_MODEL {
    checks:
      calibration_identifier
      certificate
      date
      method
      reference_standards
      uncertainty
      validity
      applicability

    RULE:
      calibration_record MUST be applicable_to_instrument_configuration_andtime
  }

  CALIBRATION_EXPIRY_MODEL {
    status:
      valid
      due
      overdue
      invalid
      unknown

    RULE:
      overdue_orunknown_status MUST be surfaced
  }

  ADJUSTMENT_EVENT_MODEL {
    adjustment:
      changes_instrument_response

    RULE:
      calibration_before_material_adjustment MAY no_longer_be_applicable
  }

  TRACEABILITY_VALIDATION_MODEL {
    checks:
      stated_reference
      calibration_chain
      uncertainty_contributions
      scope
      evidence

    RULE:
      traceability != certificate_presence_orSI_unit_usage
  }

  BROKEN_TRACEABILITY_MODEL {
    condition:
      missing_orinvalid_chain_link

    RULE:
      traceability_claim MUST be downgraded_orrejected
  }

  REFERENCE_STANDARD_VALIDATION_MODEL {
    checks:
      identifier
      value
      unit
      uncertainty
      calibration
      validity

    RULE:
      invalid_reference_standard_affects_dependent_results
  }

  CRM_VALIDATION_MODEL {
    checks:
      CRM_identifier
      batch
      certificate_version
      certified_property
      value_status
      uncertainty
      validity
      intended_use

    RULE:
      noncertified_or expired_value MUST not_be treated_as_current_certified_reference
  }

  UNCERTAINTY_VALIDATION_MODEL {
    checks:
      uncertainty_type
      value
      unit
      coverage
      components
      covariance
      method

    RULE:
      uncertainty MUST be semantically_compatible_with_measurement_result
  }

  UNCERTAINTY_UNIT_MODEL {
    absolute_uncertainty:
      same_dimension_as_measurand

    relative_uncertainty:
      dimensionless

    RULE:
      mismatch MUST be flagged
  }

  COVERAGE_VALIDATION_MODEL {
    expanded_uncertainty:
      requires_coverage_factor_or equivalent_basis

    RULE:
      expanded_uncertainty_without_coverage_context_is_incomplete
  }

  UNCERTAINTY_BUDGET_VALIDATION_MODEL {
    checks:
      component_sources
      estimates
      distributions
      sensitivity_coefficients
      covariance
      combination
      coverage

    RULE:
      omitted_material_component MAY invalidate_uncertainty_claim
  }

  COVARIANCE_VALIDATION_MODEL {
    matrix:
      symmetric
      dimensionally_consistent
      variable_order_known

    RULE:
      covariance_source SHOULD be documented
  }

  POSITIVE_SEMIDEFINITE_MODEL {
    covariance_matrix:
      should_be_positive_semidefinite_within_numeric_tolerance

    RULE:
      invalid_covariance_matrix MUST be flagged
  }

  STATISTICAL_VALIDATION_MODEL {
    checks:
      sample_size
      estimator
      independence
      distribution
      weights
      missingness
      outliers
      confidence_interval

    RULE:
      statistical_assumptions MUST be explicit
  }

  REPEATABILITY_VALIDATION_MODEL {
    repeated_values:
      dispersion

    RULE:
      repeatability_conditions MUST be declared
  }

  REPRODUCIBILITY_VALIDATION_MODEL {
    changed_conditions:
      identified

    RULE:
      reproducibility_statistic_without_conditions_is_incomplete
  }

  OUTLIER_VALIDATION_MODEL {
    observation:
      unusual_relative_to_declared_model

    RULE:
      outlier != invalid_measurement
  }

  OUTLIER_EXCLUSION_MODEL {
    exclusion:
      requires_rule
      reason
      provenance

    RULE:
      original_observation MUST be preserved
  }

  MISSING_DATA_MODEL {
    statuses:
      missing
      not_measured
      invalid
      censored
      below_detection
      above_range

    RULE:
      missing != zero
  }

  CENSORING_VALIDATION_MODEL {
    threshold:
      lower_orupper

    RULE:
      censored_result MUST preserve_threshold_andstatus
  }

  TRANSFORMATION_VALIDATION_MODEL {
    transformations:
      conversion
      correction
      filtering
      averaging
      scaling
      normalisation
      interpolation
      regression

    RULE:
      every_derived_value MUST have_resolvable_transformation_lineage
  }

  LINEAGE_VALIDATION_MODEL {
    graph:
      source
      -> activity
      -> derived_result

    checks:
      missing_parent
      cycle
      wrong_order
      version_mismatch

    RULE:
      broken_lineage MUST be flagged
  }

  RAW_DERIVED_DISTINCTION_MODEL {
    raw:
      original_acquisition

    derived:
      transformed_or calculated

    RULE:
      derived_data MUST not_be relabelled_asraw
  }

  ALGORITHM_VALIDATION_MODEL {
    checks:
      algorithm_identifier
      version
      parameters
      numerical_tolerance
      test_status

    RULE:
      algorithm_change MAY require_revalidation
  }

  SOFTWARE_VALIDATION_MODEL {
    checks:
      software_identifier
      version
      configuration
      dependencies
      validation_status

    RULE:
      software_state MUST be linked_to_processing_event
  }

  NUMERICAL_VALIDATION_MODEL {
    checks:
      overflow
      underflow
      convergence
      rounding
      precision
      conditioning

    RULE:
      numeric_success != semantic_validity
  }

  ROUND_TRIP_VALIDATION_MODEL {
    transformation:
      forward_then_inverse

    expectation:
      original_recovered_within_declared_tolerance

    RULE:
      useful_for_conversion_andserialisation_validation
  }

  PROVENANCE_VALIDATION_MODEL {
    checks:
      source
      measurement_event
      agents
      method
      instrument
      transformations
      versions
      timestamps
      evidence

    RULE:
      provenance != metrological_traceability
  }

  SOURCE_VALIDATION_MODEL {
    checks:
      source_identifier
      source_type
      authority
      version
      integrity

    RULE:
      source_authority_is_claim_specific
  }

  INTEGRITY_VALIDATION_MODEL {
    checks:
      checksum
      hash
      signature
      audit_trail

    RULE:
      integrity_validation != measurement_accuracy_validation
  }

  HASH_VALIDATION_MODEL {
    expected_hash:
      H_expected

    actual_hash:
      H_actual

    valid:
      H_expected = H_actual

    RULE:
      hash_match_confirms_canonical_data_integrity_only
  }

  DIGITAL_SIGNATURE_MODEL {
    checks:
      signer
      certificate
      signature
      timestamp

    RULE:
      valid_signature_does_not_prove_metrological_correctness
  }

  AUDIT_TRAIL_MODEL {
    checks:
      creation
      modification
      review
      approval
      deletion_orarchive

    RULE:
      material_record_changes SHOULD be reconstructable
  }

  VERSION_VALIDATION_MODEL {
    entities:
      schema
      ontology
      method
      software
      calibration
      standard
      data

    RULE:
      incompatible_or missing_version_dependencies MUST be flagged
  }

  STANDARDS_VALIDATION_MODEL {
    checks:
      standard_identifier
      edition
      scope
      clause
      status

    RULE:
      current_latest_standard_is_not_automatically_applicable_to_historical_record
  }

  SI_VALIDATION_MODEL {
    checks:
      unit_definition
      prefix
      quantity_kind
      version_context

    RULE:
      SI_usage_does_not_by_itself_establish_traceability
  }

  ISO_VALIDATION_MODEL {
    context:
      laboratory
      measurement_management
      quantities_andunits
      method

    RULE:
      ISO_standard_scope_andedition MUST be preserved
  }

  BIPM_VALIDATION_MODEL {
    context:
      SI
      comparisons
      traceability
      CMC
      KCDB

    RULE:
      CMC != individual_calibration_result
  }

  NIST_VALIDATION_MODEL {
    context:
      calibration
      SRM
      reference_data
      measurement_services

    RULE:
      name_only_NIST_traceable_claim_is_insufficient
  }

  SEMANTIC_CONFLICT_MODEL {
    examples:
      value_unit_mismatch
      quantity_kind_dimension_mismatch
      conflicting_calibration
      conflicting_timestamp
      conflicting_reference

    RULE:
      conflict MUST be preserved_until_resolved
  }

  CONFLICT_RESOLUTION_MODEL {
    compare:
      source
      scope
      time
      version
      authority
      provenance

    result:
      resolved
      partially_resolved
      unresolved

    RULE:
      AI MUST not fabricate_consensus
  }

  SEMANTIC_CONFIDENCE_MODEL {
    inputs:
      identity
      completeness
      consistency
      source_quality
      provenance
      standards_alignment

    RULE:
      semantic_confidence != measurement_uncertainty
  }

  VALIDATION_CONFIDENCE_MODEL {
    confidence:
      support_for_validation_interpretation

    RULE:
      confidence MUST fall_when_required_evidence_is_missing
  }

  VALIDATION_SEVERITY_MODEL {
    levels:
      information
      warning
      error
      critical

    RULE:
      severity MUST be rule_defined
  }

  QUALITY_FLAG_MODEL {
    possible:
      valid
      warning
      invalid
      incomplete
      out_of_range
      dimension_mismatch
      unit_unknown
      calibration_invalid
      traceability_broken
      uncertainty_incomplete
      provenance_incomplete
      lineage_broken
      standards_context_unknown
      conflict_unresolved
      manual_review

    RULE:
      flags MUST remain_linked_to_specific_validation_rules
  }

  DECISION_MODEL {
    inputs:
      rule_results
      severity
      intended_use
      mandatory_requirements
      evidence

    output:
      validation_status

    RULE:
      overall_status MUST be explainable_from_component_checks
  }

  FAIL_FAST_MODEL {
    possible:
      structural_corruption
      impossible_unit
      invalid_identity

    RULE:
      system_MAY stop_downstream_computation_but MUST preserve_all_detected_evidence
  }

  PARTIAL_VALIDITY_MODEL {
    measurement:
      may_be valid_for_one_use_but_not_another

    RULE:
      validity_is_scoped_not_absolute
  }

  INTENDED_USE_MODEL {
    fields:
      purpose      decision_context
      required_uncertainty
      range
      environment
      population
      temporal_scope

    RULE:
      validation_without_intended_use_is_incomplete
  }

  REVALIDATION_MODEL {
    triggers:
      metadata_change
      calibration_update
      method_revision
      standards_revision
      uncertainty_revision
      provenance_correction
      new_evidence

    RULE:
      dependent_validation_results MUST be recomputed_when_material_inputs_change
  }

  DEPENDENCY_GRAPH_MODEL {
    node:
      measurement_orvalidation_entity

    edge:
      depends_on

    RULE:
      dependency_graph SHOULD identify_affected_results_after_revision
  }

  RECURSIVE_VALIDATION_MODEL {
    process:
      validate_inputs
      validate_transformations
      validate_output
      propagate_flags
      revisit_after_update

    RULE:
      invalid_parent MAY invalidate_orqualify_descendants
  }

  FLAG_PROPAGATION_MODEL {
    parent_flag:
      may_propagate_to_child

    RULE:
      propagation_policy MUST depend_on_flag_type_andtransformation
  }

  EXPLAINABILITY_MODEL {
    validation_output:
      rule
      evidence
      expected
      observed
      severity
      outcome
      remediation_orrequired_context

    RULE:
      AI MUST distinguish_fact_inference_andvalidation_judgment
  }

  HUMAN_REVIEW_MODEL {
    trigger:
      unresolved_conflict
      ambiguous_quantity
      missing_reference
      unusual_result
      high_impact_failure

    RULE:
      human_review_status MUST be recorded_without_overwriting_machine_result
  }

  OVERRIDE_MODEL {
    manual_override:
      reviewer
      reason
      timestamp
      scope

    RULE:
      override MUST remain_auditable
  }

  VALIDATION_REPORT_MODEL {
    fields:
      validation_identifier
      target_identifier
      intended_use
      rule_set
      evidence
      component_results
      quality_flags
      semantic_confidence
      overall_status
      reviewer
      timestamp
      versions
      provenance

    RULE:
      report MUST support_reconstruction_of_decision
  }

  RELATIONSHIPS {
    AIMeasurementValidation -> EVALUATES -> ValidationTarget
    ValidationRule -> APPLIES_TO -> MeasurementEntity
    ValidationRule -> REQUIRES -> ValidationEvidence
    ValidationResult -> GENERATED_BY -> ValidationActivity
    ValidationResult -> QUALIFIES -> MeasurementRecord
    CalibrationRecord -> SUPPORTS -> CalibrationValidation
    TraceabilityChain -> SUPPORTS -> TraceabilityValidation
    UncertaintyBudget -> SUPPORTS -> UncertaintyValidation
    ProvenanceRecord -> SUPPORTS -> ProvenanceValidation
    TransformationHistory -> SUPPORTS -> LineageValidation
    QualityFlag -> GENERATED_FROM -> ValidationRule
  }

  SEMANTIC_EQUIVALENCE {
    "AI measurement validation" ~= machine_interpretable_validation_of_measurement_semantics
    "validation rule" ~= explicit_constraint_for_measurement_assessment
    "validation evidence" ~= source_information_supporting_validation_result

    structural_validity != measurement_validity
    dimensional_consistency != semantic_correctness
    unit_match != quantity_kind_match
    calibration != traceability
    provenance != traceability
    integrity != measurement_accuracy
    semantic_confidence != measurement_uncertainty
    outlier != invalid_measurement
    valid_signature != valid_measurement
    latest_standard != automatically_applicable_standard
  }

  CONSTRAINTS {
    validation_target_must_be identified = true
    intended_use_should_be defined = true
    validation_rules_must_be explicit = true
    quantity_kind_unit_anddimension_must_be mutually_consistent = true
    calibration_state_must_be applicable_atmeasurement_time = true
    traceability_claim_requires_documented_chain_andreference = true
    uncertainty_type_unit_andcoverage_must_be valid = true
    covariance_order_anddependencies_must_be preserved = true
    derived_values_must_have_resolvable_lineage = true
    provenance_andtraceability_must_be distinguished = true
    integrity_checks_must_not_be equated_withmeasurement_accuracy = true
    conflicts_must_not_be silently_resolved = true
    validation_status_must_be scoped_to_intended_use = true
    revisions_must_trigger_revalidation_when_dependencies_change = true
    provenance_must_be preserved = true
  }

  MEASUREMENT_MODEL {
    RECORD:
      validation_identifier
      target_identifier
      target_type
      intended_use
      measurement_identifier
      measurand_identifier
      quantity_kind_identifier
      numerical_value
      unit_identifier
      dimension_vector
      instrument_identifier
      sensor_identifier
      calibration_identifier
      traceability_reference
      uncertainty_identifier
      covariance_identifier
      method_identifier
      software_identifier
      algorithm_identifier
      timestamp
      spatial_context
      environment_identifiers
      sample_identifier
      raw_data_identifier
      transformation_history
      provenance_identifier
      standards_context
      rule_set_identifier
      component_results
      conflicts
      quality_flags
      semantic_confidence
      overall_status
      review_status
      validation_timestamp
      ontology_version
      provenance

    RULE:
      validation_record MUST remain_linked_to_target_evidence_rules_versions_andprovenance
  }

  REASONING_RULES {
    IF measurement_record_received
      THEN identify_target_type
      resolve_required_rule_set
      validate_identity_structure_andcore_semantics

    IF quantity_kind_andunit_known
      THEN derive_dimension
      AND compare_with_expected_quantity_dimension

    IF dimensions_match_but_quantity_kinds_differ
      THEN reject_semantic_equivalence_without_contextual_relation

    IF calibration_identifier_present
      THEN verify_applicability_atmeasurement_time
      and_validate_reference_uncertainty_andconfiguration

    IF traceability_claimed
      THEN traverse_reference_chain
      and_flag_missing_orinvalid_links

    IF uncertainty_reported
      THEN validate_type_unit_coverage_components_andcovariance

    IF derived_value
      THEN traverse_transformation_lineage_to_raw_orreference_inputs
      and_validate_each_material_step

    IF source_conflict_detected
      THEN preserve_all_claims
      evaluate_scope_time_version_authority_andprovenance
      and_mark_unresolved_when_evidence_insufficient

    IF mandatory_rule_fails
      THEN overall_status_cannot_be_unqualified_valid

    IF evidence_missing
      THEN use_incomplete_orindeterminate_status
      rather_than_assuming_pass

    IF dependency_changes
      THEN recursively_revalidate_affected_results

    IF human_override_applied
      THEN preserve_original_machine_validation
      attach_override_reason_andscope
  }

  VALIDATION {
    CHECK target_identity
    CHECK intended_use
    CHECK schema_andrequired_fields
    CHECK measurand
    CHECK quantity_kind
    CHECK numerical_value
    CHECK unit
    CHECK dimension
    CHECK compound_unit_structure
    CHECK conversion_semantics
    CHECK range_andphysical_bounds
    CHECK temporal_spatial_andenvironmental_context
    CHECK sample_identity
    CHECK instrument_and sensor_state
    CHECK calibration_applicability
    CHECK traceability_chain
    CHECK reference_standard_orCRM
    CHECK uncertainty_type_unit_coverage_andbudget
    CHECK covariance
    CHECK statistical_assumptions
    CHECK missingness_censoring_andoutliers
    CHECK transformation_lineage
    CHECK software_algorithm_andnumerics
    CHECK provenance
    CHECK integrity
    CHECK versions
    CHECK standards_context
    CHECK conflicts
    CHECK semantic_confidence
    CHECK quality_flags
    CHECK decision_logic
    CHECK review_oroverride
  }

  CONFIDENCE {
    identity_validation_confidence: 1.00
    unit_dimension_validation_confidence: 1.00
    quantity_kind_validation_confidence: 1.00
    calibration_traceability_validation_confidence: 1.00
    uncertainty_validation_confidence: 1.00
    provenance_lineage_validation_confidence: 1.00
    contextual_validation_confidence: 0.99
    AI_semantic_resolution_confidence: 0.98

    RULE:
      confidence decreases when identity, quantity kind, unit,
      calibration, uncertainty, traceability, context,
      lineage, version or provenance is missing or ambiguous
  }

  UPDATE_MODEL {
    ON new_validation:
      identify_target
      identify_intended_use
      load_rule_set
      gather_evidence
      execute_component_checks
      generate_quality_flags
      calculate_validation_confidence
      issue_scoped_status
      preserve_provenance

    ON measurement_revision:
      preserve_previous_measurement
      identify_changed_fields
      traverse_dependency_graph
      revalidate_affected_checks
      version_validation_result

    ON calibration_ortraceability_revision:
      preserve_previous_state
      update_evidence
      identify_dependent_measurements
      recursively_revalidate

    ON uncertainty_revision:
      preserve_previous_budget
      update_components_covariance_orcoverage
      recompute_uncertainty_validation
      propagate_status_changes

    ON standards_orontology_revision:
      preserve_historical_rule_context
      register_new_version
      determine_whether_current_records_require_revalidation
      do_not_rewrite_historical_validation_without_policy

    ON semantic_revision:
      preserve_previous_definition
      record_revision
      recompute_FLOW_HASH
  }

  AI_CONSUMPTION {
    canonical_entity: AIMeasurementValidation
    canonical_role: MachineInterpretableMeasurementAssessment

    interpretation_order:
      identify_validation_target
      identify_intended_use
      resolve_measurement_identity
      validate_structure
      resolve_measurand_andquantity_kind
      validate_value_unit_anddimension
      validate_range_andphysical_constraints
      validate_time_space_environment_and sample
      validate_instrument_sensor_andcalibration
      validate_traceability_andreferences
      validate_uncertainty_andcovariance
      validate_statistics
      validate_transformations_andlineage
      validate_provenance_integrity_andversions
      validate_standards_context
      detect_conflicts
      compute_semantic_confidence
      generate_rule_specific_flags
      derive_scoped_overall_status
      preserve_explanation_andprovenance

    ambiguity_controls:
      distinguish_structural_validity_from_measurement_validity
      distinguish_dimensional_consistency_from_semantic_correctness
      distinguish_unit_match_from_quantity_kind_match
      distinguish_calibration_from_traceability
      distinguish_provenance_from_traceability
      distinguish_integrity_from_measurement_accuracy
      distinguish_semantic_confidence_from_measurement_uncertainty
      distinguish_outlier_from_invalid_measurement
      distinguish_valid_signature_from_valid_measurement
      distinguish_latest_standard_from_applicable_standard
      preserve_target_use_rule_evidence_context_uncertainty_lineage_version_conflict_andprovenance
  }

  PROVENANCE {
    ontology_scope:
      mathematical_measurement_units_AI_measurement_semantics_andmeasurement_validation

    parent_path:
      Ontologies
      -> Mathematical Ontologies
      -> Measurement & Units Ontologies
      -> AI Measurement Semantics
      -> Measurement Validation Ontology

    authorship:
      Mark Higgins
      Sky (Recursive AI)

    site:
      https://kirethresearch.org
  }

  FLOW_HASH {
    INPUTS:
      canonical_entity
      ontology_path
      validation_pipeline_model
      identity_validation_model
      duplicate_identity_model
      structural_validation_model
      required_field_model
      datatype_model
      measurand_validation_model
      quantity_kind_validation_model
      numerical_value_validation_model
      finite_value_model
      unit_validation_model
      unit_symbol_ambiguity_model
      dimensional_validation_model
      same_dimension_different_kind_model
      compound_unit_validation_model
      unit_conversion_validation_model
      affine_quantity_model
      range_validation_model
      physical_bound_model
      plausibility_model
      context_completeness_model
      temporal_validation_model
      spatial_validation_model
      environment_validation_model
      sample_validation_model
      instrument_validation_model
      sensor_validation_model
      calibration_validation_model
      calibration_expiry_model
      adjustment_event_model
      traceability_validation_model
      broken_traceability_model
      reference_standard_validation_model
      CRM_validation_model
      uncertainty_validation_model
      uncertainty_unit_model
      coverage_validation_model
      uncertainty_budget_validation_model
      covariance_validation_model
      positive_semidefinite_model
      statistical_validation_model
      repeatability_validation_model
      reproducibility_validation_model
      outlier_validation_model
      outlier_exclusion_model
      missing_data_model
      censoring_validation_model
      transformation_validation_model
      lineage_validation_model
      raw_derived_distinction_model
      algorithm_validation_model
      software_validation_model
      numerical_validation_model
      round_trip_validation_model
      provenance_validation_model
      source_validation_model
      integrity_validation_model
      hash_validation_model
      digital_signature_model
      audit_trail_model
      version_validation_model
      standards_validation_model
      SI_validation_model
      ISO_validation_model
      BIPM_validation_model
      NIST_validation_model
      semantic_conflict_model
      conflict_resolution_model
      semantic_confidence_model
      validation_confidence_model
      validation_severity_model
      quality_flag_model
      decision_model
      fail_fast_model
      partial_validity_model
      intended_use_model
      revalidation_model
      dependency_graph_model
      recursive_validation_model
      flag_propagation_model
      explainability_model
      human_review_model
      override_model
      validation_report_model
      relationships
      semantic_equivalence
      constraints
      measurement_model
      reasoning_rules
      confidence_model
      update_model

    PURPOSE:
      deterministic_semantic_integrity_identifier

    RECOMPUTE_WHEN:
      ontology_definition_changes
      validation_rule_orintended_use_changes
      identity_structure_orquantity_semantics_change
      unit_dimension_range_orcontext_validation_changes
      instrument_calibration_ortraceability_changes
      uncertainty_covariance_orstatistical_validation_changes
      transformation_lineage_oralgorithm_changes
      provenance_integrity_orversion_validation_changes
      standards_context_orconflict_resolution_changes
      confidence_decision_orquality_flag_logic_changes
      dependency_orrecursive_revalidation_model_changes
      measurement_record_changes
      constraint_model_changes
      reasoning_rule_changes
  }

}

END FLOW