Measurement Metadata Ontology

# Measurement Metadata Ontology — Flow Algebra

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

FLOW MeasurementMetadataOntology {

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

  PURPOSE:
    Represent the metadata required to make measurement information machine-interpretable, semantically resolvable, reproducible, comparable, traceable and suitable for recursive AI reasoning. The ontology models measurement identifiers, measurands, quantity kinds, numerical values, units, dimensions, uncertainty, covariance, instruments, sensors, calibration, methods, standards, reference materials, temporal and spatial context, environmental influence quantities, sampling, processing, quality flags, validation, provenance, data lineage, versioning, semantic confidence, ambiguity and transformation history. It ensures that a measurement is represented not merely as a number but as a structured evidence-bearing entity whose meaning and derivation can be reconstructed.

  ENTITY MeasurementMetadata {
    TYPE: SemanticMeasurementRecord

    PROPERTIES:
      identifies_measurement
      identifies_measurand
      identifies_quantity_kind
      identifies_value
      identifies_unit
      identifies_dimension
      identifies_uncertainty
      identifies_instrument
      identifies_method
      identifies_calibration
      identifies_traceability
      identifies_time
      identifies_space
      identifies_environment
      identifies_processing
      identifies_quality
      identifies_provenance
      identifies_versions
  }

  ENTITY MeasurementIdentifier {
    TYPE: PersistentIdentifier

    RULE:
      identifier MUST uniquely_resolve_measurement_record_within_declared_namespace
  }

  ENTITY MetadataField {
    TYPE: SemanticAttribute

    COMPONENTS:
      identifier
      name
      datatype
      unit_when_applicable
      cardinality
      requirement_status
      semantic_definition
      source
  }

  CORE_METADATA_MODEL {
    required_or_core:
      measurement_identifier
      measurand
      quantity_kind
      numerical_value
      unit
      timestamp_orobservation_context
      provenance

    conditional:
      uncertainty
      calibration
      traceability
      instrument
      environment
      method
      quality_flags

    RULE:
      requiredness_depends_on_measurement_context_and_intended_use
  }

  IDENTIFIER_MODEL {
    measurement_identifier:
      stable_machine_reference

    possible:
      URI
      UUID
      database_key
      domain_identifier

    RULE:
      display_name != persistent_identity
  }

  NAMESPACE_MODEL {
    identifier:
      interpreted_with_namespace

    RULE:
      local_identifier_without_namespace MAY be_nonunique
  }

  MEASURAND_METADATA_MODEL {
    fields:
      measurand_identifier
      quantity_kind
      object_or_system
      property
      conditions
      matrix_or medium
      temporal_context
      spatial_context

    RULE:
      measurand_definition MUST be sufficiently_specific_for_interpretation
  }

  QUANTITY_KIND_MODEL {
    fields:
      quantity_kind_identifier
      canonical_name
      aliases
      dimension
      ontology_reference

    RULE:
      quantity_kind != dimension
  }

  NUMERICAL_VALUE_MODEL {
    possible:
      integer
      decimal
      floating_point
      rational
      complex
      vector
      tensor
      interval
      distribution

    RULE:
      datatype MUST not_replace_quantity_semantics
  }

  VALUE_REPRESENTATION_MODEL {
    fields:
      stored_value
      original_text
      precision
      significant_figures
      scientific_notation
      encoding

    RULE:
      original_representation SHOULD be preserved_when_reporting_semantics_matter
  }

  UNIT_METADATA_MODEL {
    fields:
      unit_identifier
      unit_name
      unit_symbol
      unit_system
      prefix
      scale
      offset
      dimension

    RULE:
      unit_symbol_alone MAY be_insufficient_for_machine_resolution
  }

  UNIT_IDENTIFIER_MODEL {
    identifier:
      stable_reference_to_unit_definition

    RULE:
      identifier SHOULD distinguish_units_with_same_orambiguous_symbols
  }

  DIMENSION_METADATA_MODEL {
    fields:
      dimension_identifier
      base_dimension_vector
      basis_order

    canonical:
      [L,M,T,I,Theta,N,J]

    RULE:
      basis_order MUST be preserved
  }

  DIMENSIONAL_VALIDATION_MODEL {
    quantity_kind:
      expected_dimension

    unit:
      derived_dimension

    RULE:
      mismatch MUST produce_validation_flag
  }

  SAME_DIMENSION_DIFFERENT_KIND_MODEL {
    examples:
      energy_and_torque
      frequency_and_activity
      gray_and_sievert_related_quantities

    RULE:
      metadata MUST preserve_quantity_kind_not_only_dimension
  }

  VALUE_UNIT_LINK_MODEL {
    quantity_value:
      numerical_value+unit

    RULE:
      value_and_unit MUST be atomically_orrelationally_linked
  }

  UNCERTAINTY_METADATA_MODEL {
    fields:
      uncertainty_identifier
      uncertainty_type
      numerical_value
      unit
      distribution
      coverage_factor
      coverage_probability
      degrees_of_freedom
      method
      components

    RULE:
      uncertainty_type MUST be explicit
  }

  STANDARD_UNCERTAINTY_MODEL {
    type:
      standard_uncertainty

    unit:
      same_as_measurand
  }

  EXPANDED_UNCERTAINTY_MODEL {
    type:
      expanded_uncertainty

    fields:
      U
      coverage_factor
      coverage_basis

    RULE:
      U_without_coverage_context_is_incomplete
  }

  RELATIVE_UNCERTAINTY_MODEL {
    type:
      relative

    dimension:
      1

    RULE:
      denominator_reference MUST be identifiable
  }

  UNCERTAINTY_COMPONENT_MODEL {
    fields:
      source
      estimate
      distribution
      standard_uncertainty
      sensitivity
      contribution
      degrees_of_freedom
      provenance
  }

  COVARIANCE_METADATA_MODEL {
    fields:
      variable_identifiers
      covariance_matrix
      variable_order
      units
      source

    RULE:
      matrix_order MUST be preserved
  }

  CORRELATION_METADATA_MODEL {
    fields:
      variable_identifiers
      correlation_matrix
      order

    RULE:
      correlation_matrix != covariance_matrix
  }

  ERROR_METADATA_MODEL {
    fields:
      error_estimate
      reference
      correction
      correction_status

    RULE:
      error != uncertainty
  }

  PRECISION_METADATA_MODEL {
    fields:
      repeatability
      reproducibility
      standard_deviation
      conditions

    RULE:
      precision_conditions MUST be specified
  }

  ACCURACY_METADATA_MODEL {
    representation:
      qualitative_orquantified_against_declared_reference_metric

    RULE:
      accuracy MUST not_be encoded_as_synonym_for_uncertainty
  }

  RESOLUTION_METADATA_MODEL {
    fields:
      nominal_resolution
      effective_resolution
      unit
      conditions

    RULE:
      resolution != uncertainty
  }

  INSTRUMENT_METADATA_MODEL {
    fields:
      instrument_identifier
      instrument_type
      manufacturer
      model
      serial_number
      range
      resolution
      firmware
      location
      status

    RULE:
      instrument_state_at_measurement_time MUST be recoverable
  }

  SENSOR_METADATA_MODEL {
    fields:
      sensor_identifier
      sensor_type
      transduction_principle
      range
      sensitivity
      calibration_identifier

    RULE:
      sensor_identity SHOULD be distinct_from_host_instrument_when_relevant
  }

  CHANNEL_METADATA_MODEL {
    fields:
      channel_identifier
      input
      gain
      offset
      range
      sampling_rate
      filter
      ADC_resolution

    RULE:
      channel_configuration MUST be versioned
  }

  CALIBRATION_METADATA_MODEL {
    fields:
      calibration_identifier
      certificate_identifier
      calibration_date
      laboratory
      method
      reference_standards
      results
      uncertainty
      validity
      next_review_orstatus

    RULE:
      calibration_metadata MUST link_to_state_applicable_at_measurement_time
  }

  TRACEABILITY_METADATA_MODEL {
    fields:
      traceability_reference
      chain_identifiers
      calibration_links
      uncertainty_contributions
      standards_context

    RULE:
      traceability != certificate_identifier_alone
  }

  REFERENCE_STANDARD_MODEL {
    fields:
      standard_identifier
      quantity_kind
      assigned_value
      unit
      uncertainty
      calibration_status
      traceability
  }

  CRM_METADATA_MODEL {
    fields:
      CRM_identifier
      batch
      certificate_version
      certified_property
      certified_value
      uncertainty
      traceability
      expiry

    RULE:
      CRM_batch_and_certificate_version MUST be preserved
  }

  METHOD_METADATA_MODEL {
    fields:
      method_identifier
      name
      version
      procedure_reference
      measurement_equation
      valid_range
      corrections
      validation_status

    RULE:
      method_version MUST be linked_to_result
  }

  MEASUREMENT_EQUATION_MODEL {
    fields:
      equation_identifier
      expression
      input_quantity_identifiers
      output_quantity_identifier
      constants
      corrections
      parameter_versions

    RULE:
      equation MUST support_reconstruction_of_derived_result
  }

  INPUT_QUANTITY_MODEL {
    fields:
      identifier
      value
      unit
      uncertainty
      covariance_links
      source

    RULE:
      derived_result MUST preserve_input_lineage
  }

  CORRECTION_METADATA_MODEL {
    fields:
      correction_identifier
      value
      unit
      uncertainty
      sign_convention
      reason
      source

    RULE:
      correction MUST be distinguishable_from_raw_indication
  }

  ENVIRONMENT_METADATA_MODEL {
    possible:
      temperature
      humidity
      pressure
      vibration
      electromagnetic_field
      supply_voltage
      contamination
      light

    RULE:
      material_influence_quantities SHOULD be linked_to_measurement_event
  }

  ENVIRONMENT_SENSOR_MODEL {
    field:
      environmental_value

    metadata:
      sensor_identifier
      calibration
      uncertainty
      timestamp

    RULE:
      environmental_measurement_itself_requires_measurement_metadata
  }

  TEMPORAL_METADATA_MODEL {
    fields:
      observation_time
      acquisition_start
      acquisition_end
      duration
      time_zone
      time_scale
      clock_identifier
      timestamp_uncertainty

    RULE:
      time_semantics MUST be explicit_when_material
  }

  TIME_SCALE_MODEL {
    possible:
      UTC
      TAI
      GPS_time
      local_clock
      domain_specific

    RULE:
      timestamp_string_without_time_scale MAY be ambiguous
  }

  SPATIAL_METADATA_MODEL {
    fields:
      location_identifier
      coordinate_reference_system
      coordinates
      altitude
      depth
      spatial_extent
      location_uncertainty

    RULE:
      coordinate_reference_system MUST accompany_coordinates_when_needed
  }

  SAMPLE_METADATA_MODEL {
    fields:
      sample_identifier
      material
      matrix
      batch
      preparation
      collection_time
      collection_location
      storage
      chain_of_custody

    RULE:
      sample_identity MUST remain_linked_to_measurement_result
  }

  SPECIMEN_MODEL {
    specimen:
      physical_item_measured

    fields:
      identifier
      dimensions
      orientation
      condition
      preparation

    RULE:
      specimen_state MAY affect_measurand
  }

  SAMPLING_METADATA_MODEL {
    fields:
      sampling_method
      sampling_rate
      sampling_interval
      sample_count
      trigger
      window
      missing_samples

    RULE:
      sampling_configuration MUST be preserved
  }

  DIGITAL_SAMPLING_MODEL {
    fields:
      sample_rate
      ADC_bits
      full_scale
      anti_alias_filter
      clock

    RULE:
      digital_code != physical_quantity_without_scaling_and_calibration
  }

  DATA_ACQUISITION_MODEL {
    fields:
      acquisition_system
      software
      channel
      configuration
      start_time
      end_time
      raw_file

    RULE:
      acquisition_configuration MUST be linked_to_raw_data
  }

  RAW_DATA_MODEL {
    fields:
      raw_data_identifier
      encoding
      checksum
      acquisition_metadata
      storage_location

    RULE:
      raw_data MUST remain_immutable_orversioned
  }

  PROCESSED_DATA_MODEL {
    fields:
      processed_data_identifier
      parent_data
      transformations
      algorithm
      parameters
      version

    RULE:
      processed_data MUST preserve_lineage_to_raw_data
  }

  TRANSFORMATION_MODEL {
    possible:
      unit_conversion
      calibration
      correction
      filtering
      smoothing
      averaging
      normalisation
      scaling
      interpolation
      resampling
      regression

    RULE:
      transformations MUST be ordered
  }

  TRANSFORMATION_STEP_MODEL {
    fields:
      step_identifier
      input_identifiers
      operation
      parameters
      algorithm_version
      output_identifier
      timestamp
      provenance
  }

  UNIT_CONVERSION_METADATA_MODEL {
    fields:
      source_unit
      target_unit
      factor
      offset
      exactness
      conversion_source

    RULE:
      conversion MUST preserve_underlying_quantity
  }

  FILTER_METADATA_MODEL {
    fields:
      filter_type
      coefficients
      cutoff
      order
      phase
      sampling_rate

    RULE:
      filter_parameters MUST be sufficient_for_reproduction
  }

  AVERAGING_METADATA_MODEL {
    fields:
      averaging_method
      window
      weights
      sample_count

    RULE:
      mean_without_input_count_ormethod MAY be insufficient
  }

  NORMALISATION_METADATA_MODEL {
    fields:
      method
      reference
      centre
      scale
      range
      parameter_source

    RULE:
      normalised_value MUST retain_parameters_needed_for_interpretation
  }

  STATISTICAL_METADATA_MODEL {
    fields:
      estimator
      sample_size
      distribution
      degrees_of_freedom
      confidence_level
      covariance
      assumptions

    RULE:
      statistical_result MUST identify_estimator_and_model
  }

  VALIDATION_METADATA_MODEL {
    fields:
      validation_identifier
      criterion
      reference
      result
      decision_rule
      scope
      evidence

    RULE:
      validation_status MUST be scoped
  }

  QUALITY_FLAG_MODEL {
    fields:
      flag_identifier
      severity
      reason
      source
      timestamp
      resolution_status

    possible:
      valid
      warning
      invalid
      missing
      out_of_range
      overrange
      calibration_issue
      traceability_issue
      extrapolated
      manually_reviewed

    RULE:
      quality_flag MUST not_delete_original_measurement
  }

  MISSING_VALUE_MODEL {
    statuses:
      missing
      not_measured
      not_applicable
      invalid
      censored
      below_detection
      above_range

    RULE:
      missing_status MUST be separate_from_numeric_value
  }

  CENSORING_METADATA_MODEL {
    fields:
      censor_type
      threshold
      unit
      method

    RULE:
      censored_value MUST not_be represented_as_exact_threshold_value_without_flag
  }

  DETECTION_LIMIT_MODEL {
    fields:
      detection_limit
      quantification_limit
      method
      unit
      confidence_orcriterion

    RULE:
      detection_limit_metadata MUST be method_specific
  }

  RANGE_METADATA_MODEL {
    fields:
      instrument_range
      method_valid_range
      calibrated_range
      reported_range

    RULE:
      range_types MUST be distinguished
  }

  PROVENANCE_METADATA_MODEL {
    fields:
      creator
      organisation
      source_system
      acquisition_event
      processing_agent
      reviewer
      publication
      timestamps
      signatures

    RULE:
      provenance MUST support_chain_of_responsibility_and_derivation
  }

  DATA_LINEAGE_MODEL {
    graph:
      raw_measurement
      -> corrections
      -> conversions
      -> processing
      -> derived_result
      -> validation
      -> publication

    RULE:
      lineage MUST be traversable_both_forward_and_backward
  }

  PARENT_CHILD_MODEL {
    parent:
      source_measurement_or dataset

    child:
      derived_measurement

    RULE:
      many_to_many_lineage MUST be supported
  }

  VERSION_METADATA_MODEL {
    fields:
      ontology_version
      schema_version
      method_version
      software_version
      calibration_version
      standards_version
      data_version

    RULE:
      versions MUST be preserved_for_reproducibility
  }

  SCHEMA_VERSION_MODEL {
    schema:
      measurement_metadata_schema

    RULE:
      migration MUST preserve_semantics_orrecord_loss
  }

  STANDARDS_METADATA_MODEL {
    possible:
      SI_version
      ISO_standard
      IEC_standard
      BIPM_document
      NIST_resource
      laboratory_standard
      domain_standard

    RULE:
      standard_identifier_and_version MUST be explicit
  }

  SEMANTIC_IDENTIFIER_MODEL {
    entities:
      quantity_kind
      unit
      instrument
      method
      standard
      CRM
      location
      organisation

    RULE:
      machine_identifier SHOULD be preferred_over_free_text_when_available
  }

  ONTOLOGY_REFERENCE_MODEL {
    field:
      ontology_URI_orentity_identifier

    RULE:
      ontology_reference MUST identify_version_orstable_semantic_identity
  }

  SEMANTIC_CONFIDENCE_MODEL {
    fields:
      confidence_value
      confidence_scale
      evidence
      inference_method

    RULE:
      semantic_confidence != measurement_uncertainty
  }

  AMBIGUITY_METADATA_MODEL {
    fields:
      ambiguity_status
      candidate_entities
      candidate_units
      candidate_quantity_kinds
      resolution_evidence

    RULE:
      unresolved_ambiguity MUST be preserved
  }

  INFERENCE_METADATA_MODEL {
    fields:
      inferred_field
      inferred_value
      rule_identifier
      evidence
      confidence
      timestamp

    RULE:
      inferred_metadata MUST be distinguishable_from_observed_orprovided_metadata
  }

  OBSERVED_VS_INFERRED_MODEL {
    statuses:
      observed
      provided
      derived
      inferred
      defaulted

    RULE:
      origin_status MUST accompany_semantically_material_fields
  }

  DEFAULT_VALUE_MODEL {
    default:
      schema_orcontext_defined

    RULE:
      defaulted_value MUST not_be presented_as_observed_fact
  }

  CONFLICT_METADATA_MODEL {
    fields:
      conflicting_claims
      sources
      versions
      resolution_status
      selected_interpretation

    RULE:
      conflict MUST remain_auditable
  }

  SOURCE_AUTHORITY_MODEL {
    source:
      measurement_record
      calibration_certificate
      standard
      manufacturer
      user
      inferred_context

    RULE:
      source_authority_is_contextual_and_should_be_recorded
  }

  ACCESS_CONTROL_MODEL {
    metadata:
      public
      restricted
      confidential

    RULE:
      access_control MUST not alter_underlying_measurement_semantics
  }

  INTEGRITY_MODEL {
    fields:
      checksum
      hash
      signature
      audit_trail

    RULE:
      integrity_metadata_supports_trust_but_does_not_establish_metrological_traceability
  }

  CHECKSUM_MODEL {
    file_orrecord:
      cryptographic_orother_checksum

    RULE:
      checksum_verifies_data_integrity_not_measurement_accuracy
  }

  AUDIT_TRAIL_MODEL {
    events:
      creation
      modification
      review
      approval
      export
      correction

    RULE:
      previous_values SHOULD remain_recoverable
  }

  DIGITAL_SIGNATURE_MODEL {
    purpose:
      authenticate_record_orapproval

    RULE:
      digital_signature != metrological_validation
  }

  SERIALISATION_MODEL {
    possible:
      JSON
      JSON_LD
      XML
      RDF
      CSV
      binary

    RULE:
      serialisation MUST preserve_types_identifiers_units_and_lineage
  }

  JSON_MODEL {
    numeric_value:
      may_lose_decimal_reporting_semantics_if_parser_converts_types

    RULE:
      significant_figures_and_original_representation SHOULD be separate_metadata
  }

  RDF_MODEL {
    node:
      entity

    edge:
      typed_relationship

    RULE:
      URI_identity_and_graph_direction MUST be preserved
  }

  JSON_LD_MODEL {
    context:
      maps_fields_to_semantic_identifiers

    RULE:
      context_version SHOULD be controlled
  }

  CSV_MODEL {
    flat_table:
      may_require_external_schema

    RULE:
      units_uncertainty_andidentifiers SHOULD not_be implicit_in_column_names_only
  }

  DATABASE_MODEL {
    relational:
      normalized_entities_andforeign_keys

    graph:
      nodes_andrelationships

    document:
      nested_measurement_record

    RULE:
      storage_model MUST preserve_semantic_links
  }

  NULL_MODEL {
    null:
      absence_of_value

    RULE:
      null != zero
      null != not_applicable
      null != below_detection
  }

  CARDINALITY_MODEL {
    field:
      one
      zero_orone
      many

    RULE:
      schema_cardinality MUST reflect_measurement_semantics
  }

  REQUIRED_FIELD_MODEL {
    requirement:
      depends_on_record_type_andintended_use

    RULE:
      validation SHOULD distinguish_missing_required_from_optional_metadata
  }

  EXTENSION_MODEL {
    domain:
      may_add_specialised_fields

    RULE:
      extensions MUST not redefine_core_fields_incompatibly
  }

  INTEROPERABILITY_MODEL {
    exchange:
      measurement_record_between_systems

    requirements:
      stable_identifiers
      units
      quantity_kinds
      uncertainty
      provenance
      versions

    RULE:
      syntactic_exchange != semantic_interoperability
  }

  ROUND_TRIP_MODEL {
    serialise:
      record -> exchange_format

    parse:
      exchange_format -> record

    expectation:
      semantic_equivalence

    RULE:
      round_trip_test SHOULD verify_no_material_semantic_loss
  }

  HUMAN_READABLE_MODEL {
    display:
      labels
      units
      formatted_values
      explanations

    RULE:
      human_display SHOULD be generated_from_semantic_record_not_replace_it
  }

  MACHINE_READABLE_MODEL {
    representation:
      typed_identifiers_andvalues

    RULE:
      machine_record SHOULD avoid_ambiguous_free_text_where_structured_semantics_exist
  }

  EXPLAINABILITY_MODEL {
    metadata:
      supports_explanation_of
        what_was_measured
        how
        where
        when
        with_what
        against_what_reference
        with_what_uncertainty
        how_processed
        why_flagged

    RULE:
      AI_explanation MUST distinguish_source_metadata_from_inference
  }

  RELATIONSHIPS {
    MeasurementMetadata -> DESCRIBES -> MeasurementResult
    MeasurementResult -> ESTIMATES -> Measurand
    MeasurementResult -> HAS_VALUE -> QuantityValue
    QuantityValue -> HAS -> NumericalValue
    QuantityValue -> HAS -> Unit
    QuantityKind -> HAS_DIMENSION -> Dimension
    MeasurementResult -> HAS_UNCERTAINTY -> MeasurementUncertainty
    MeasurementResult -> PRODUCED_BY -> Instrument
    Instrument -> HAS_CALIBRATION -> CalibrationRecord
    CalibrationRecord -> TRACEABLE_TO -> Reference
    MeasurementResult -> GENERATED_BY -> Method
    MeasurementResult -> OBSERVED_AT -> TimeAndLocation
    DerivedMeasurement -> DERIVED_FROM -> SourceMeasurement
    TransformationStep -> CONNECTS -> InputAndOutputData
    QualityFlag -> QUALIFIES -> MeasurementRecord
  }

  SEMANTIC_EQUIVALENCE {
    "measurement metadata" ~= structured_semantic_context_describing_measurement_information
    "measurement identifier" ~= persistent_reference_to_measurement_record
    "data lineage" ~= graph_of_derivation_from_source_to_derived_data

    metadata != measurement_value
    numerical_value != quantity
    unit != dimension
    quantity_kind != dimension
    uncertainty != error
    calibration_identifier != traceability
    timestamp != temporal_accuracy
    quality_flag != measurement_value
    semantic_confidence != measurement_uncertainty
    inferred_metadata != observed_metadata
    checksum != measurement_validation
  }

  CONSTRAINTS {
    measurement_identifier_must_be_preserved = true
    measurand_and_quantity_kind_should_be_identified = true
    numerical_value_and_unit_must_remain_linked = true
    quantity_kind_and_dimension_must_be_distinguished = true
    uncertainty_type_must_be explicit_when_uncertainty_reported = true
    covariance_variable_order_must_be preserved = true
    instrument_and_calibration_state_should_be linked_to_measurement_time = true
    method_and_software_versions_must_be preserved = true
    derived_data_must_preserve_lineage = true
    quality_flags_must_not_delete_raw_values = true
    missing_censored_andzero_values_must_be distinguished = true
    inferred_andobserved_metadata_must_be distinguished = true
    unresolved_ambiguity_must_be preserved = true
    historical_versions_must_be preserved = true
    provenance_must_be preserved = true
  }

  MEASUREMENT_MODEL {
    RECORD:
      measurement_identifier
      record_type
      source_identifier
      measurand_identifier
      quantity_kind_identifier
      numerical_value
      original_representation
      value_datatype
      unit_identifier
      unit_symbol
      unit_system
      dimension_vector
      standard_uncertainty
      expanded_uncertainty
      coverage_factor
      covariance_identifier
      instrument_identifier
      sensor_identifier
      channel_identifier
      calibration_identifier
      traceability_reference
      reference_standard_identifiers
      CRM_identifier
      method_identifier
      method_version
      measurement_equation_identifier
      input_quantity_identifiers
      correction_identifiers
      observation_time
      time_scale
      spatial_context
      environment_identifiers
      sample_identifier
      sampling_metadata
      raw_data_identifier
      transformation_history
      processed_data_identifier
      validation_identifier
      quality_flags
      missing_orcensoring_status
      semantic_confidence
      ambiguity_status
      inference_records
      ontology_version
      schema_version
      software_version
      standards_versions
      checksum
      audit_trail
      provenance

    RULE:
      metadata_record MUST support_reconstruction_of_measurement_meaning_origin_and_processing
  }

  REASONING_RULES {
    IF measurement_record_created
      THEN assign_persistent_identifier
      AND attach_measurand_quantity_kind_value_unit_time_andprovenance

    IF unit_identifier_known
      THEN derive_dimension
      AND validate_against_quantity_kind

    IF uncertainty_reported
      THEN identify_uncertainty_type_unit_coverage_andcomponents

    IF covariance_matrix_attached
      THEN preserve_variable_identifiers_andorder

    IF instrument_identifier_known
      THEN resolve_instrument_state_andcalibration_at_observation_time

    IF calibration_identifier_known
      THEN resolve_reference_standards_uncertainty_andtraceability_context

    IF derived_value_created
      THEN create_transformation_step
      link_all_inputs
      preserve_algorithm_andparameters

    IF metadata_inferred
      THEN mark_origin_as_inferred
      attach_rule_evidence_andsemantic_confidence

    IF source_conflict_detected
      THEN create_conflict_record
      preserve_claims_sources_andversions

    IF measurement_invalid_orout_of_range
      THEN attach_quality_flag
      and_preserve_original_value

    IF record_serialised_andreparsed
      THEN validate_semantic_round_trip

    IF schema_orontology_updated
      THEN preserve_previous_version
      migrate_with_explicit_mapping
      and_record_any_semantic_loss
  }

  VALIDATION {
    CHECK measurement_identifier
    CHECK namespace
    CHECK record_type
    CHECK measurand
    CHECK quantity_kind
    CHECK numerical_value
    CHECK unit_identifier_and_symbol
    CHECK dimension
    CHECK quantity_kind_dimension_consistency
    CHECK uncertainty_type_andcoverage
    CHECK covariance_order
    CHECK instrument_sensor_andchannel
    CHECK calibration_state
    CHECK traceability_reference
    CHECK reference_standard_orCRM
    CHECK method_andversion
    CHECK measurement_equation
    CHECK temporal_metadata
    CHECK spatial_metadata
    CHECK environmental_metadata
    CHECK sampling
    CHECK raw_data_integrity
    CHECK transformation_history
    CHECK validation_andquality_flags
    CHECK missing_andcensoring_status
    CHECK observed_vs_inferred_origin
    CHECK ambiguity
    CHECK semantic_confidence
    CHECK schema_ontology_software_andstandards_versions
    CHECK audit_trail
    CHECK provenance
  }

  CONFIDENCE {
    core_metadata_confidence: 1.00
    quantity_unit_dimension_confidence: 1.00
    uncertainty_metadata_confidence: 1.00
    calibration_traceability_metadata_confidence: 1.00
    temporal_spatial_environment_confidence: 0.99
    lineage_andtransformation_confidence: 1.00
    interoperability_confidence: 0.99
    inferred_metadata_confidence: 0.97

    RULE:
      confidence decreases when identifier, measurand, unit,
      method, calibration, uncertainty, time, lineage,
      version or provenance is missing or ambiguous
  }

  UPDATE_MODEL {
    ON new_measurement_metadata:
      assign_identifier
      populate_core_fields
      resolve_semantic_identifiers
      attach_context_uncertainty_andprovenance
      validate_record
      compute_FLOW_HASH

    ON metadata_enrichment:
      preserve_existing_fields
      add_new_context
      mark_field_origin
      update_semantic_confidence
      revalidate_dependencies

    ON metadata_correction:
      preserve_previous_value
      record_reason_source_andtimestamp
      update_field
      recompute_dependent_inferences
      update_audit_trail

    ON transformation:
      create_transformation_step
      link_inputs_outputs_algorithm_andparameters
      update_lineage
      preserve_raw_data

    ON schema_revision:
      preserve_previous_schema
      map_fields_to_new_schema
      record_migration
      flag_unmapped_orlossy_fields

    ON semantic_revision:
      preserve_previous_ontology_mapping
      record_revision
      recompute_FLOW_HASH
  }

  AI_CONSUMPTION {
    canonical_entity: MeasurementMetadata
    canonical_role: SemanticMeasurementRecord

    interpretation_order:
      resolve_measurement_identifier
      identify_record_type
      identify_measurand
      identify_quantity_kind
      parse_numerical_value_andrepresentation
      resolve_unit
      derive_andvalidate_dimension
      resolve_uncertainty_andcovariance
      resolve_instrument_sensor_channel_andcalibration
      resolve_traceability_reference
      resolve_method_equation_andinputs
      resolve_time_space_environment_and sample
      resolve_sampling_andacquisition
      resolve_raw_andprocessed_data
      reconstruct_transformation_lineage
      resolve_validation_quality_andmissingness
      distinguish_observed_provided_derived_inferred_anddefaulted_metadata
      resolve_ambiguity_andconfidence
      resolve_versions_integrity_andprovenance
      validate_semantic_completeness
      expose_machine_andhuman_interpretable_measurement_context

    ambiguity_controls:
      distinguish_metadata_from_measurement_value
      distinguish_numerical_value_from_quantity
      distinguish_unit_from_dimension
      distinguish_quantity_kind_from_dimension
      distinguish_uncertainty_from_error
      distinguish_calibration_identifier_from_traceability
      distinguish_timestamp_from_timing_accuracy
      distinguish_quality_flag_from_measurement_value
      distinguish_semantic_confidence_from_measurement_uncertainty
      distinguish_inferred_metadata_from_observed_metadata
      distinguish_checksum_from_measurement_validation
      preserve_identifier_value_unit_context_uncertainty_lineage_origin_version_andprovenance
  }

  PROVENANCE {
    ontology_scope:
      mathematical_measurement_units_AI_measurement_semantics_andmeasurement_metadata

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

    authorship:
      Mark Higgins
      Sky (Recursive AI)

    site:
      https://kirethresearch.org
  }

  FLOW_HASH {
    INPUTS:
      canonical_entity
      ontology_path
      core_metadata_model
      identifier_model
      namespace_model
      measurand_metadata_model
      quantity_kind_model
      numerical_value_model
      value_representation_model
      unit_metadata_model
      unit_identifier_model
      dimension_metadata_model
      dimensional_validation_model
      same_dimension_different_kind_model
      value_unit_link_model
      uncertainty_metadata_model
      standard_uncertainty_model
      expanded_uncertainty_model
      relative_uncertainty_model
      uncertainty_component_model
      covariance_metadata_model
      correlation_metadata_model
      error_metadata_model
      precision_metadata_model
      accuracy_metadata_model
      resolution_metadata_model
      instrument_metadata_model
      sensor_metadata_model
      channel_metadata_model
      calibration_metadata_model
      traceability_metadata_model
      reference_standard_model
      CRM_metadata_model
      method_metadata_model
      measurement_equation_model
      input_quantity_model
      correction_metadata_model
      environment_metadata_model
      environment_sensor_model
      temporal_metadata_model
      time_scale_model
      spatial_metadata_model
      sample_metadata_model
      specimen_model
      sampling_metadata_model
      digital_sampling_model
      data_acquisition_model
      raw_data_model
      processed_data_model
      transformation_model
      transformation_step_model
      unit_conversion_metadata_model
      filter_metadata_model
      averaging_metadata_model
      normalisation_metadata_model
      statistical_metadata_model
      validation_metadata_model
      quality_flag_model
      missing_value_model
      censoring_metadata_model
      detection_limit_model
      range_metadata_model
      provenance_metadata_model
      data_lineage_model
      parent_child_model
      version_metadata_model
      schema_version_model
      standards_metadata_model
      semantic_identifier_model
      ontology_reference_model
      semantic_confidence_model
      ambiguity_metadata_model
      inference_metadata_model
      observed_vs_inferred_model
      default_value_model
      conflict_metadata_model
      source_authority_model
      access_control_model
      integrity_model
      checksum_model
      audit_trail_model
      digital_signature_model
      serialisation_model
      JSON_model
      RDF_model
      JSON_LD_model
      CSV_model
      database_model
      null_model
      cardinality_model
      required_field_model
      extension_model
      interoperability_model
      round_trip_model
      human_readable_model
      machine_readable_model
      explainability_model
      relationships
      semantic_equivalence
      constraints
      measurement_model
      reasoning_rules
      confidence_model
      update_model

    PURPOSE:
      deterministic_semantic_integrity_identifier

    RECOMPUTE_WHEN:
      ontology_definition_changes
      core_identifier_measurand_orquantity_metadata_changes
      unit_dimension_oruncertainty_metadata_changes
      instrument_calibration_ortraceability_metadata_changes
      method_equation_environment_time_orlocation_changes
      sampling_acquisition_orraw_data_changes
      transformation_lineage_orprocessed_data_changes
      validation_quality_missingness_orcensoring_changes
      inference_ambiguity_orconfidence_changes
      schema_serialisation_orinteroperability_changes
      version_integrity_orprovenance_changes
      measurement_record_changes
      constraint_model_changes
      reasoning_rule_changes
  }

}

END FLOW