Measurement Uncertainty

# Measurement Uncertainty Ontology — Flow Algebra

URL: https://kirethresearch.org/booksai-core/ontologies/mathematical-ontologies/measurement-units-ontologies/measurement-uncertainty.html

FLOW MeasurementUncertaintyOntology {

  DOMAIN:
    Mathematical Ontologies
    -> Measurement & Units Ontologies
    -> Measurement Uncertainty Ontology

  PURPOSE:
    Represent measurement uncertainty as quantified doubt associated with measurement results, including measurands, estimates, standard uncertainty, Type A and Type B evaluation, probability distributions, covariance, correlation, uncertainty propagation, sensitivity coefficients, combined standard uncertainty, expanded uncertainty, coverage factors, confidence or coverage intervals, calibration uncertainty, resolution, repeatability, reproducibility, systematic effects, bias corrections, uncertainty budgets, Monte Carlo propagation, traceability, significant figures, provenance, and strict distinctions between uncertainty, error, tolerance, accuracy, precision and variability.

  ENTITY MeasurementUncertainty {
    TYPE: MeasurementQualityQuantity

    DEFINITION:
      measurement_uncertainty :=
        nonnegative_parameter_characterising_dispersion_of_quantity_values
        attributed_to_a_measurand
        based_on_information_used

    PROPERTIES:
      is_nonnegative
      qualifies_measurement_result
      depends_on_measurement_model
      depends_on_information_and_assumptions
      may_be_standard_or_expanded
      may_include_covariance
      has_unit_related_to_measurand_or_dimensionless_relative_form
  }

  ENTITY Measurand {
    TYPE: QuantityIntendedToBeMeasured

    REQUIRE:
      quantity_definition
      conditions
      reference_state_when_relevant

    RULE:
      uncertainty_evaluation_requires_defined_measurand
  }

  ENTITY MeasurementResult {
    TYPE: QuantityEstimateWithAssociatedInformation

    COMPONENTS:
      measured_quantity_value
      measurement_uncertainty
      unit
      measurand_definition
      coverage_information_when_relevant
      provenance
  }

  ENTITY StandardUncertainty {
    TYPE: StandardDeviationLikeUncertainty
    SYMBOL: u
  }

  ENTITY CombinedStandardUncertainty {
    TYPE: StandardUncertainty
    SYMBOL: u_c
  }

  ENTITY ExpandedUncertainty {
    TYPE: CoverageUncertainty
    SYMBOL: U

    RELATION:
      U =
        k*u_c
      under_declared_coverage_factor
  }

  TERMINOLOGY_MODEL {
    terms:
      uncertainty
      standard_uncertainty
      combined_standard_uncertainty
      expanded_uncertainty
      error
      bias
      correction
      accuracy
      precision
      repeatability
      reproducibility
      tolerance

    RULE:
      these_terms MUST_NOT be_treated_as_semantically_interchangeable
  }

  STANDARD_UNCERTAINTY_MODEL {
    standard_uncertainty:
      u(x)

    interpretation:
      uncertainty_expressed_as_standard_deviation

    RULE:
      standard_uncertainty_has_same_unit_as_quantity
  }

  RELATIVE_UNCERTAINTY_MODEL {
    relative_standard_uncertainty:
      u_r(x) =
        u(x)/abs(x)
      WHEN x != 0

    percent_uncertainty:
      100*u_r(x)

    RULE:
      relative_uncertainty_is_dimensionless
  }

  TYPE_A_MODEL {
    definition:
      uncertainty_component_evaluated_by_statistical_analysis_of_series_of_observations

    sample_mean:
      x_bar =
        sum(x_i)/n

    sample_standard_deviation:
      s =
        sqrt(
          sum((x_i-x_bar)^2)/(n-1)
        )
      WHEN n > 1

    standard_uncertainty_of_mean:
      u_A =
        s/sqrt(n)

    RULE:
      Type_A_refers_to_evaluation_method_not_error_type
  }

  TYPE_B_MODEL {
    definition:
      uncertainty_component_evaluated_by_means_other_than_statistical_analysis_of_repeated_observations

    possible_sources:
      calibration_certificate
      instrument_specification
      resolution
      previous_data
      reference_material
      physical_constants
      expert_information

    RULE:
      Type_B_requires_probability_distribution_or_equivalent_standard_uncertainty_assignment
  }

  RECTANGULAR_DISTRIBUTION_MODEL {
    half_width:
      a

    standard_uncertainty:
      u =
        a/sqrt(3)

    CONDITIONS:
      quantity_equally_likely_within_minus_a_to_plus_a

    RULE:
      distribution_assumption MUST be preserved
  }

  TRIANGULAR_DISTRIBUTION_MODEL {
    half_width:
      a

    standard_uncertainty:
      u =
        a/sqrt(6)

    CONDITIONS:
      symmetric_triangular_distribution

    RULE:
      triangular_and_rectangular_distributions_produce_different_uncertainties
  }

  NORMAL_DISTRIBUTION_MODEL {
    standard_deviation:
      sigma

    standard_uncertainty:
      u =
        sigma
      when_distribution_standard_deviation_represents_input_uncertainty

    RULE:
      confidence_or_coverage_information MUST be converted_consistently_to_standard_uncertainty
  }

  RESOLUTION_MODEL {
    instrument_resolution:
      delta

    common_rounding_assumption:
      error_interval =
        +/- delta/2

    rectangular_standard_uncertainty:
      u_res =
        delta/sqrt(12)

    RULE:
      resolution_value != standard_uncertainty
  }

  CALIBRATION_CERTIFICATE_MODEL {
    reported:
      expanded_uncertainty U_cal
      coverage_factor k_cal

    standard:
      u_cal =
        U_cal/k_cal
      WHEN k_cal != 0

    RULE:
      certificate_coverage_factor_and_scope MUST be preserved
  }

  CORRECTION_MODEL {
    observed_or_indicated:
      x_ind

    correction:
      C

    corrected:
      x_corr =
        x_ind+C

    RULE:
      applying_known_correction_does_not_remove_uncertainty_of_correction
  }

  BIAS_MODEL {
    bias:
      expected_or_estimated_systematic_difference
      between_measurement_and_reference_or_true_value_under_model

    correction:
      may_be_applied_for_estimated_bias

    RULE:
      bias != uncertainty
  }

  ERROR_DISTINCTION_MODEL {
    measurement_error:
      measured_value-minus_reference_or_true_value
      under_defined_reference

    uncertainty:
      quantified_dispersion_of_values_attributed_to_measurand

    RULE:
      measurement_error != measurement_uncertainty
  }

  ACCURACY_DISTINCTION_MODEL {
    accuracy:
      qualitative_or_contextual_closeness_of_agreement_to_true_or_reference_value

    RULE:
      accuracy SHOULD_NOT be_used_as_numeric_synonym_for_uncertainty
  }

  PRECISION_DISTINCTION_MODEL {
    precision:
      closeness_of_agreement_between_repeated_indications_or_values_under_specified_conditions

    related:
      repeatability
      reproducibility

    RULE:
      precision != accuracy
      and_precision != total_measurement_uncertainty
  }

  REPEATABILITY_MODEL {
    conditions:
      same_method
      same_operator_or_system
      same_instrument
      same_location
      short_time_interval
      other_declared_repeatability_conditions

    statistic:
      repeatability_standard_deviation

    RULE:
      repeatability_is_one_possible_uncertainty_component
  }

  REPRODUCIBILITY_MODEL {
    conditions:
      changed_laboratory_operator_instrument_time_or_other_declared_conditions

    statistic:
      reproducibility_standard_deviation

    RULE:
      reproducibility_context MUST be defined
  }

  TOLERANCE_DISTINCTION_MODEL {
    tolerance:
      permitted_limit_or_range_for_product_process_orquantity

    uncertainty:
      property_of_measurement_result

    RULE:
      tolerance != measurement_uncertainty
  }

  MEASUREMENT_MODEL_FUNCTION {
    general:
      Y =
        f(X_1,X_2,...,X_n)

    output_estimate:
      y =
        f(x_1,x_2,...,x_n)

    RULE:
      uncertainty_propagation_requires_model_and_input_uncertainties
  }

  SENSITIVITY_COEFFICIENT_MODEL {
    coefficient:
      c_i =
        partial_f/partial_X_i

    evaluated_at:
      input_estimates

    RULE:
      sensitivity_coefficient_maps_input_uncertainty_to_output_effect
  }

  LAW_OF_PROPAGATION_MODEL {
    uncorrelated:
      u_c^2(y) =
        sum(
          c_i^2*u^2(x_i)
        )

    correlated:
      u_c^2(y) =
        sum_i sum_j(
          c_i*c_j*u(x_i,x_j)
        )

    covariance_form:
      u_c^2(y) =
        J*Sigma*J_transpose

    RULE:
      covariance_terms MUST be included_when_inputs_are_correlated
  }

  COVARIANCE_MODEL {
    covariance:
      cov(X_i,X_j)

    correlation:
      r_ij =
        cov(X_i,X_j)/
        (u_i*u_j)
      WHEN u_i != 0 AND u_j != 0

    bounds:
      -1 <= r_ij <= 1

    RULE:
      zero_covariance_and_independence_are_not_identical_in_general
  }

  SUM_DIFFERENCE_MODEL {
    Y =
      A +/- B

    independent:
      u_Y =
        sqrt(u_A^2+u_B^2)

    correlated:
      include_covariance_term_with_sign

    RULE:
      uncertainties_are_not_normally_added_arithmetically_for_independent_inputs
  }

  PRODUCT_QUOTIENT_MODEL {
    Y =
      A*B
      or
      A/B

    small_uncertainty_independent_relative_form:
      (u_Y/Y)^2 approximately_equals
        (u_A/A)^2+(u_B/B)^2

    RULE:
      approximation_requires_nonzero_inputs_and_linearisation_validity
  }

  POWER_MODEL {
    Y =
      A^n

    relative:
      u_Y/abs(Y) approximately_equals
        abs(n)*u_A/abs(A)

    RULE:
      first_order_approximation_requires_small_uncertainty
  }

  NONLINEAR_MODEL {
    issue:
      first_order_linearisation_may_be_inadequate

    alternatives:
      higher_order_propagation
      Monte_Carlo
      numerical_sampling
      Bayesian_propagation

    RULE:
      propagation_method SHOULD match_model_nonlinearity_and_distribution_shape
  }

  MONTE_CARLO_MODEL {
    process:
      sample_inputs_from_joint_distribution
      evaluate_measurement_model
      construct_output_distribution

    outputs:
      estimate
      standard_uncertainty
      coverage_interval
      distribution_shape

    REQUIRE:
      input_distributions
      correlations
      sufficient_samples
      random_or_quasi_random_method

    RULE:
      Monte_Carlo_result MUST preserve_input_model_and_sampling_configuration
  }

  COVERAGE_FACTOR_MODEL {
    expanded_uncertainty:
      U =
        k*u_c

    common:
      k approximately_equals 2
      may_correspond_to_about_95_percent_coverage
      only_under_appropriate_distribution_and_degrees_of_freedom

    RULE:
      coverage_factor_does_not_have_universal_fixed_probability
  }

  COVERAGE_INTERVAL_MODEL {
    interval:
      [y-U_minus, y+U_plus]
      or_symmetric
      [y-U,y+U]

    REQUIRE:
      coverage_probability_or_factor
      distribution_or_method

    RULE:
      coverage_interval != guaranteed_true_value_interval
  }

  DEGREES_OF_FREEDOM_MODEL {
    effective_degrees_of_freedom:
      may_be_estimated_using_Welch_Satterthwaite

    purpose:
      select_coverage_factor_from_t_distribution_when_applicable

    RULE:
      finite_degrees_of_freedom_affect_coverage_factor
  }

  WELCH_SATTERTHWAITE_MODEL {
    generic:
      nu_eff =
        u_c^4 /
        sum(
          c_i^4*u_i^4/nu_i
        )

    CONDITIONS:
      applicable_independent_components
      first_order_propagation

    RULE:
      terms_with_effectively_infinite_degrees_of_freedom_contribute_accordingly
  }

  UNCERTAINTY_BUDGET_MODEL {
    entries:
      source
      estimate
      unit
      distribution
      divisor
      standard_uncertainty
      sensitivity_coefficient
      contribution
      degrees_of_freedom
      correlation

    RULE:
      uncertainty_budget MUST preserve_each_component_and_assumption
  }

  TRACEABILITY_MODEL {
    chain:
      measurement_result
      -> calibration
      -> reference_standard
      -> higher_order_standard
      -> SI_or_declared_reference

    RULE:
      metrological_traceability_requires_documented_unbroken_calibration_chain_with_uncertainties
  }

  CALIBRATION_MODEL {
    calibration:
      establishes_relation_between_indication_and_reference_quantity_values

    outputs:
      correction
      calibration_function
      uncertainty

    RULE:
      calibration != adjustment
      and_calibration_uncertainty_propagates_to_measurement
  }

  DRIFT_MODEL {
    instrument_drift:
      change_in_indication_or_response_over_time

    correction:
      may_use_time_dependent_model

    uncertainty:
      residual_drift_uncertainty

    RULE:
      drift_model_and_calibration_interval SHOULD be preserved
  }

  ENVIRONMENTAL_MODEL {
    possible_influences:
      temperature
      pressure
      humidity
      vibration
      electromagnetic_interference
      supply_voltage

    RULE:
      environmental_sensitivity MAY contribute_to_uncertainty
  }

  SAMPLING_MODEL {
    sampling_uncertainty:
      arises_when_sample_or_observation_support_does_not_fully_represent_measurand_population_or_field

    RULE:
      sampling_uncertainty MAY dominate_in_environmental_biological_and_spatial_measurements
  }

  MODEL_UNCERTAINTY_MODEL {
    sources:
      approximations
      omitted_variables
      parameterisation
      boundary_conditions
      empirical_model_form

    RULE:
      model_uncertainty SHOULD be represented_when_material
  }

  DIGITAL_NUMERICAL_MODEL {
    possible_sources:
      floating_point
      numerical_solver_tolerance
      interpolation
      discretisation
      rounding

    RULE:
      numerical_uncertainty_should_be_distinguished_from_physical_measurement_uncertainty
  }

  SIGNIFICANT_FIGURES_MODEL {
    reporting:
      uncertainty_commonly_reported_with_limited_significant_digits
      measured_value_rounded_to_same_decimal_place

    RULE:
      rounding SHOULD_NOT imply_more_precision_than_supported_by_uncertainty
  }

  UNCERTAINTY_INTERVAL_NOTATION_MODEL {
    symmetric:
      x +/- U

    asymmetric:
      x +U_plus -U_minus

    relative:
      x with relative_uncertainty

    RULE:
      notation MUST identify_standard_or_expanded_uncertainty
  }

  LOGARITHMIC_QUANTITY_MODEL {
    example:
      magnitude
      decibel
      pH

    RULE:
      uncertainty_propagation_must_respect_logarithmic_transformation
  }

  UNIT_CONVERSION_MODEL {
    linear:
      y =
        a*x

    uncertainty:
      u_y =
        abs(a)*u_x

    affine:
      y =
        a*x+b

    uncertainty:
      u_y =
        abs(a)*u_x
      when_a_and_b_exact

    RULE:
      uncertain_conversion_constants MUST also_contribute_to_uncertainty
  }

  RELATIONSHIPS {
    MeasurementResult -> HAS -> MeasurementUncertainty
    MeasurementUncertainty -> QUALIFIES -> MeasuredQuantityValue
    StandardUncertainty -> IS_A -> MeasurementUncertainty
    CombinedStandardUncertainty -> COMBINES -> InputUncertaintyComponents
    ExpandedUncertainty -> DERIVED_FROM -> CombinedStandardUncertainty
    CoverageFactor -> SCALES -> CombinedStandardUncertainty
    MeasurementModel -> MAPS -> InputQuantitiesToMeasurandEstimate
    SensitivityCoefficient -> MAPS -> InputUncertaintyContribution
    Covariance -> RELATES -> CorrelatedInputUncertainties
    Calibration -> CONTRIBUTES_TO -> MeasurementUncertainty
    TraceabilityChain -> PROPAGATES -> CalibrationUncertainty
  }

  SEMANTIC_EQUIVALENCE {
    "standard uncertainty" ~= standard_deviation_form_uncertainty
    "combined standard uncertainty" ~= propagated_standard_uncertainty_under_declared_model

    uncertainty != error
    uncertainty != bias
    uncertainty != tolerance
    uncertainty != accuracy
    uncertainty != precision
    repeatability != reproducibility
    standard_uncertainty != expanded_uncertainty
    coverage_interval != guaranteed_error_bound
  }

  CONSTRAINTS {
    measurand_must_be_defined = true
    uncertainty_must_be_nonnegative = true
    uncertainty_unit_must_match_quantity_or_be_declared_relative = true
    standard_and_expanded_uncertainty_are_not_semantically_equivalent = true
    coverage_factor_must_be_declared_for_expanded_uncertainty = true
    correlations_must_be_included_when_material = true
    correction_and_uncertainty_must_be_distinguished = true
    Type_A_and_Type_B_are_evaluation_methods_not_error_classes = true
    uncertainty_and_error_are_not_semantically_equivalent = true
    uncertainty_budget_assumptions_should_be_preserved = true
  }

  MEASUREMENT_MODEL {
    RECORD:
      measurand
      measured_value
      unit
      measurement_model
      input_quantities
      input_estimates
      standard_uncertainties
      distributions
      correlations
      sensitivity_coefficients
      uncertainty_contributions
      combined_standard_uncertainty
      degrees_of_freedom
      coverage_factor
      expanded_uncertainty
      coverage_probability_when_available
      corrections
      calibration_information
      traceability
      environmental_conditions
      sampling_model
      numerical_model
      reporting_precision
      provenance

    RESULT:
      y +/- U
      WITH
        unit
        coverage_factor_or_probability
        uncertainty_method
  }

  REASONING_RULES {
    IF repeated_observations_known
      AND n > 1
      THEN Type_A_standard_uncertainty_of_mean =
        sample_standard_deviation/sqrt(n)

    IF rectangular_half_width_known
      THEN standard_uncertainty =
        half_width/sqrt(3)

    IF triangular_half_width_known
      THEN standard_uncertainty =
        half_width/sqrt(6)

    IF calibration_expanded_uncertainty_and_coverage_factor_known
      AND coverage_factor != 0
      THEN calibration_standard_uncertainty =
        expanded_uncertainty/coverage_factor

    IF measurement_model_and_input_uncertainties_known
      THEN compute_sensitivity_coefficients
      AND propagate_covariance

    IF inputs_uncorrelated
      THEN combined_variance =
        sum(c_i^2*u_i^2)

    IF covariance_matrix_known
      THEN combined_variance =
        J*Sigma*J_transpose

    IF combined_standard_uncertainty      AND coverage_factor_known
      THEN expanded_uncertainty =
        coverage_factor*combined_standard_uncertainty

    IF quantity_converted_by_exact_linear_factor
      THEN converted_uncertainty =
        abs(conversion_factor)*original_uncertainty

    IF nonlinear_model_or_nonGaussian_inputs_material
      THEN MAY use_Monte_Carlo_propagation
      preserving_joint_input_distribution

    IF known_bias_correction_applied
      THEN preserve_correction_uncertainty
      in_combined_uncertainty_budget
  }

  VALIDATION {
    CHECK measurand_definition
    CHECK measured_value_and_unit
    CHECK uncertainty_nonnegative
    CHECK standard_vs_expanded_uncertainty
    CHECK Type_A_vs_Type_B_evaluation
    CHECK probability_distribution_assumptions
    CHECK calibration_certificate_coverage_factor
    CHECK correction_vs_bias_vs_uncertainty
    CHECK covariance_and_correlation
    CHECK sensitivity_coefficients
    CHECK propagation_model
    CHECK nonlinear_model_validity
    CHECK coverage_factor_and_probability
    CHECK degrees_of_freedom
    CHECK uncertainty_budget
    CHECK traceability
    CHECK sampling_and_model_uncertainty
    CHECK reporting_precision
    CHECK provenance
  }

  CONFIDENCE {
    definition_confidence: 1.00
    standard_uncertainty_confidence: 1.00
    Type_A_relationship_confidence: 1.00
    propagation_relationship_confidence: 1.00
    covariance_model_confidence: 1.00
    expanded_uncertainty_confidence: 1.00
    Monte_Carlo_model_confidence: 0.99
    measurement_context_confidence: 0.99

    RULE:
      confidence decreases when measurand, distribution assumptions,
      correlations, calibration, model form, coverage factor,
      sampling context or provenance is missing or ambiguous
  }

  UPDATE_MODEL {
    ON new_measurement:
      identify_measurand
      identify_measured_value_and_unit
      identify_measurement_model
      enumerate_uncertainty_sources
      classify_evaluation_methods
      assign_standard_uncertainties
      attach_distributions_and_correlations
      compute_combined_standard_uncertainty
      compute_expanded_uncertainty_when_requested
      preserve_uncertainty_budget
      attach_provenance

    ON new_calibration:
      preserve_previous_calibration
      attach_correction_and_calibration_uncertainty
      update_traceability_chain
      recompute_combined_uncertainty

    ON model_revision:
      preserve_previous_measurement_model
      update_inputs_sensitivity_coefficients_and_correlations
      recompute_uncertainty
      compare_linear_and_nonlinear_propagation_when_material

    ON new_repeated_data:
      append_observations
      recompute_mean_standard_deviation_and_Type_A_uncertainty
      update_degrees_of_freedom

    ON coverage_revision:
      preserve_combined_standard_uncertainty
      update_coverage_factor_or_interval_method
      recompute_expanded_uncertainty
      record_coverage_semantics

    ON semantic_revision:
      preserve_previous_definition
      record_revision
      recompute_FLOW_HASH
  }

  AI_CONSUMPTION {
    canonical_domain: MeasurementUncertainty

    interpretation_order:
      identify_measurand
      identify_measured_value_and_unit
      identify_uncertainty_type
      identify_standard_or_expanded_form
      identify_Type_A_and_Type_B_components
      identify_probability_distributions
      identify_measurement_model
      identify_sensitivity_coefficients
      identify_covariances_and_correlations
      identify_calibration_and_corrections
      identify_sampling_model
      identify_model_and_numerical_uncertainty
      identify_coverage_factor_or_probability
      identify_traceability
      identify_reporting_precision
      validate_uncertainty_semantics
      propagate_uncertainty

    ambiguity_controls:
      distinguish_uncertainty_from_error
      distinguish_uncertainty_from_bias
      distinguish_uncertainty_from_tolerance
      distinguish_uncertainty_from_accuracy
      distinguish_uncertainty_from_precision
      distinguish_standard_from_expanded_uncertainty
      distinguish_repeatability_from_reproducibility
      distinguish_correction_from_uncertainty_of_correction
      distinguish_coverage_interval_from_guaranteed_bound
      preserve_measurand_model_distribution_correlation_calibration_coverage_and_provenance_context
  }

  PROVENANCE {
    ontology_scope:
      mathematical_measurement_and_uncertainty_semantics

    parent_path:
      Ontologies
      -> Mathematical Ontologies
      -> Measurement & Units Ontologies
      -> Measurement Uncertainty Ontology

    authorship:
      Mark Higgins
      Sky (Recursive AI)

    site:
      https://kirethresearch.org
  }

  FLOW_HASH {
    INPUTS:
      canonical_domain
      ontology_path
      terminology_model
      standard_uncertainty_model
      relative_uncertainty_model
      Type_A_model
      Type_B_model
      rectangular_distribution_model
      triangular_distribution_model
      normal_distribution_model
      resolution_model
      calibration_certificate_model
      correction_model
      bias_model
      error_distinction_model
      accuracy_distinction_model
      precision_distinction_model
      repeatability_model
      reproducibility_model
      tolerance_distinction_model
      measurement_model_function
      sensitivity_coefficient_model
      law_of_propagation_model
      covariance_model
      sum_difference_model
      product_quotient_model
      power_model
      nonlinear_model
      Monte_Carlo_model
      coverage_factor_model
      coverage_interval_model
      degrees_of_freedom_model
      Welch_Satterthwaite_model
      uncertainty_budget_model
      traceability_model
      calibration_model
      drift_model
      environmental_model
      sampling_model
      model_uncertainty_model
      digital_numerical_model
      significant_figures_model
      uncertainty_interval_notation_model
      logarithmic_quantity_model
      unit_conversion_model
      relationships
      constraints
      measurement_model
      reasoning_rules
      confidence_model
      update_model

    PURPOSE:
      deterministic_semantic_integrity_identifier

    RECOMPUTE_WHEN:
      ontology_definition_changes
      uncertainty_definition_or_classification_changes
      probability_distribution_model_changes
      measurement_model_or_sensitivity_changes
      covariance_or_correlation_model_changes
      propagation_method_changes
      coverage_or_degrees_of_freedom_model_changes
      calibration_or_traceability_model_changes
      sampling_or_model_uncertainty_changes
      measurement_record_changes
      constraint_model_changes
      reasoning_rule_changes
  }

}

END FLOW