Measurement Uncertainty Ontology
Measurement Uncertainty
Authors: MArk Anthony Higgins - Sky Recursive Ai
Revised Date: 16 September 2026
Audio Overview
# Measurement Uncertainty Ontology — Flow Algebra
URL: https://kirethresearch.org/booksai-core/ontologies/mathematical-ontologies/measurement-units-ontologies/measurement-uncertainty/measurement-uncertainty-ontology.html
FLOW MeasurementUncertaintyOntology {
DOMAIN:
Mathematical Ontologies
-> Measurement & Units Ontologies
-> Measurement Uncertainty
-> Measurement Uncertainty Ontology
PURPOSE:
Represent measurement uncertainty as a formally structured, non-negative parameter associated with a measurement result that characterises the dispersion of quantity values reasonably attributable to a defined measurand. The ontology supports standard uncertainty, Type A and Type B evaluation, combined and expanded uncertainty, covariance and correlation, sensitivity coefficients, uncertainty budgets, probability distributions, coverage intervals, calibration, traceability, sampling and model uncertainty, Monte Carlo propagation, uncertainty updates, confidence semantics and machine-consumable reasoning while strictly distinguishing uncertainty from error, bias, tolerance, accuracy and precision.
ENTITY MeasurementUncertainty {
TYPE: MetrologicalUncertaintyQuantity
SYMBOL: u_or_U_context_dependent
DEFINITION:
nonnegative_parameter_characterising_dispersion
of_quantity_values_attributed_to_measurand
based_on_information_used
PROPERTIES:
nonnegative
measurand_dependent
model_dependent
information_dependent
may_be_absolute
may_be_relative
may_be_standard
may_be_combined
may_be_expanded
may_be_symmetric_or_asymmetric
may_have_distribution
may_have_covariance
has_provenance
}
ENTITY Measurand {
TYPE: QuantityIntendedToBeMeasured
REQUIRE:
quantity_kind
system_or_object
conditions
reference_state_when_relevant
spatial_temporal_support_when_relevant
RULE:
uncertainty_without_defined_measurand_is_semantically_incomplete
}
ENTITY MeasuredQuantityValue {
TYPE: QuantityEstimate
SYMBOL: y
REQUIRE:
numerical_value
unit
measurand
}
ENTITY StandardUncertainty {
TYPE: MeasurementUncertainty
SYMBOL: u
INTERPRETATION:
uncertainty_expressed_as_standard_deviation
}
ENTITY CombinedStandardUncertainty {
TYPE: StandardUncertainty
SYMBOL: u_c
DEFINITION:
standard_uncertainty_of_output_quantity
obtained_from_combination_of_input_uncertainty_components
}
ENTITY ExpandedUncertainty {
TYPE: MeasurementUncertainty
SYMBOL: U
FORM:
U = k*u_c
REQUIRE:
coverage_factor
}
ENTITY RelativeStandardUncertainty {
TYPE: DimensionlessUncertaintyQuantity
SYMBOL: u_r
FORM:
u_r =
u(x)/abs(x)
WHEN x != 0
}
RESULT_MODEL {
basic:
result =
measured_value
+ associated_uncertainty_information
symmetric_expanded:
Y =
y +/- U
standard:
Y =
y with_standard_uncertainty_u
asymmetric:
Y =
y +U_plus -U_minus
RULE:
uncertainty_type_and_coverage_semantics MUST accompany_reported_value
}
TERMINOLOGY_MODEL {
uncertainty:
dispersion_parameter
error:
difference_between_measured_and_reference_or_true_value_under_model
bias:
estimated_systematic_difference
correction:
quantity_added_or_applied_to_compensate_known_systematic_effect
precision:
closeness_of_repeated_measurements_under_specified_conditions
accuracy:
qualitative_closeness_to_true_or_reference_value
tolerance:
permitted_limit_or_range
RULE:
uncertainty != error
uncertainty != bias
uncertainty != accuracy
uncertainty != precision
uncertainty != tolerance
}
ABSOLUTE_UNCERTAINTY_MODEL {
quantity:
u(x)
unit:
same_as_x
RULE:
absolute_uncertainty MUST use_compatible_quantity_unit
}
RELATIVE_UNCERTAINTY_MODEL {
standard:
u_r =
u(x)/abs(x)
WHEN x != 0
percentage:
u_percent =
100*u_r
RULE:
relative_uncertainty_is_dimensionless
}
TYPE_A_EVALUATION_MODEL {
definition:
evaluation_by_statistical_analysis_of_series_of_observations
observations:
x_1,...,x_n
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)
degrees_of_freedom:
nu =
n-1
RULE:
Type_A_is_evaluation_method_not_random_error_category
}
TYPE_B_EVALUATION_MODEL {
definition:
evaluation_by_information_other_than_statistical_analysis_of_current_repeated_observations
sources:
calibration_certificate
manufacturer_specification
instrument_resolution
reference_material
previous_measurements
handbook_data
physical_constants
environmental_limits
expert_assessment
PROCESS:
identify_information
assign_probability_distribution
derive_standard_uncertainty
RULE:
Type_B_is_evaluation_method_not_systematic_error_category
}
DISTRIBUTION_MODEL {
possible:
normal
rectangular
triangular
U_shaped
Student_t
lognormal
empirical
discrete
custom
RULE:
distribution_choice MUST be justified_and_preserved
}
RECTANGULAR_MODEL {
interval:
[-a,+a]
standard_uncertainty:
u =
a/sqrt(3)
variance:
u^2 =
a^2/3
}
TRIANGULAR_MODEL {
interval:
[-a,+a]
standard_uncertainty:
u =
a/sqrt(6)
variance:
u^2 =
a^2/6
}
RESOLUTION_MODEL {
resolution_increment:
delta
rounding_interval:
+/- delta/2
rectangular_standard_uncertainty:
u_res =
delta/sqrt(12)
RULE:
resolution != uncertainty
}
CALIBRATION_UNCERTAINTY_MODEL {
certificate_expanded_uncertainty:
U_cal
certificate_coverage_factor:
k_cal
standard_uncertainty:
u_cal =
U_cal/k_cal
WHEN k_cal != 0
REQUIRE:
calibration_scope
unit
coverage_information
traceability
}
CORRECTION_MODEL {
indicated_value:
x_ind
correction:
C
corrected_value:
x_corr =
x_ind+C
uncertainty:
u(C)
RULE:
correction_value_and_uncertainty_of_correction_are_distinct
}
BIAS_MODEL {
estimated_bias:
b
corrected_result:
y_corr =
y-b
under_declared_bias_sign_convention
residual:
remaining_uncertainty_after_correction
RULE:
uncorrected_known_bias_requires_explicit_treatment_in_measurement_model_or_uncertainty_statement
}
MEASUREMENT_MODEL {
general:
Y =
f(X_1,X_2,...,X_n)
estimates:
y =
f(x_1,x_2,...,x_n)
REQUIRE:
input_quantities
input_estimates
units
uncertainty_components
dependencies
RULE:
uncertainty_propagation MUST correspond_to_declared_measurement_model
}
INPUT_QUANTITY_MODEL {
ENTITY InputQuantity {
estimate
unit
standard_uncertainty
distribution
degrees_of_freedom
provenance
}
RULE:
each_material_input SHOULD have_uncertainty_information
}
SENSITIVITY_MODEL {
sensitivity_coefficient:
c_i =
partial_f/partial_X_i
evaluated_at:
x_1,...,x_n
contribution:
u_i_y =
abs(c_i)*u(x_i)
RULE:
coefficient_unit_maps_input_quantity_to_output_quantity
}
COVARIANCE_MODEL {
covariance:
u(x_i,x_j)
correlation:
r_ij =
u(x_i,x_j)/
(u(x_i)*u(x_j))
WHEN both_standard_uncertainties_nonzero
bounds:
-1 <= r_ij <= 1
RULE:
correlated_inputs_require_covariance_terms
}
PROPAGATION_MODEL {
first_order_general:
u_c^2(y) =
sum_i(
c_i^2*u^2(x_i)
)
+
2*sum_i sum_j_greater_i(
c_i*c_j*u(x_i,x_j)
)
matrix:
variance_y =
J*Sigma*J_transpose
RULE:
linearisation_validity MUST be_assessed_for_nonlinear_models
}
INDEPENDENT_INPUT_MODEL {
condition:
covariance_terms_zero
then:
u_c(y) =
sqrt(
sum(c_i^2*u_i^2)
)
RULE:
uncorrelated_does_not_always_imply_statistical_independence
}
SUM_DIFFERENCE_MODEL {
Y =
A+B
or
A-B
independent:
u_Y =
sqrt(u_A^2+u_B^2)
correlated:
covariance_term_sign_depends_on_measurement_model
RULE:
standard_uncertainties_do_not_generally_add_linearly
}
PRODUCT_QUOTIENT_MODEL {
Y =
A*B
or
A/B
small_relative_uncertainty_independent_approximation:
(u_Y/Y)^2 =
(u_A/A)^2+
(u_B/B)^2
REQUIRE:
A != 0
B != 0
first_order_validity
}
POWER_MODEL {
Y =
A^n
first_order:
u_Y/abs(Y) =
abs(n)*u_A/abs(A)
REQUIRE:
A != 0
first_order_validity
}
CORRELATION_SOURCE_MODEL {
possible_causes:
shared_calibration_standard
common_environment
common_reference_constant
repeated_use_of_same_input
common_data_processing
mathematical_constraint
RULE:
shared_sources SHOULD trigger_correlation_review
}
COMBINED_STANDARD_UNCERTAINTY_MODEL {
output:
u_c(y)
inputs:
propagated_standard_uncertainty_components
RULE:
combined_standard_uncertainty_is_standard_deviation_form
}
COVERAGE_FACTOR_MODEL {
symbol:
k
expanded:
U =
k*u_c
RULE:
k MUST be reported_or_inferable_for_expanded_uncertainty
}
EXPANDED_UNCERTAINTY_MODEL {
U =
k*u_c
interval_common_form:
y-U <= Y <= y+U
RULE:
expanded_uncertainty_is_not_standard_uncertainty
}
COVERAGE_PROBABILITY_MODEL {
probability:
p
relation_to_k:
depends_on_distribution_and_degrees_of_freedom
RULE:
k=2 MUST_NOT automatically_be_interpreted_as_exact_95_percent_coverage
}
DEGREES_OF_FREEDOM_MODEL {
finite:
associated_with_statistically_estimated_components
infinite_or_large:
may_be_assigned_to_some_Type_B_components_under_declared_model
RULE:
degrees_of_freedom_affect_coverage_factor_when_t_distribution_used
}
WELCH_SATTERTHWAITE_MODEL {
effective:
nu_eff =
u_c^4/
sum(
c_i^4*u_i^4/nu_i
)
CONDITIONS:
applicable_independent_component_form
RULE:
effective_degrees_of_freedom_model_assumptions MUST be_preserved
}
MONTE_CARLO_MODEL {
inputs:
joint_probability_distribution_of_X
process:
sample_X
evaluate_Y=f(X)
repeat
output_distribution:
empirical_distribution_of_Y
derive:
expectation_or_estimate
standard_deviation
quantiles
coverage_interval
RULE:
Monte_Carlo_is_preferred_option_when_linearisation_or_normal_approximation_is_inadequate
}
ASYMMETRIC_UNCERTAINTY_MODEL {
lower:
U_minus
upper:
U_plus
report:
y +U_plus -U_minus
RULE:
asymmetric_distribution SHOULD_NOT be_forced_into_symmetric_interval_without_justification
}
BAYESIAN_MODEL {
posterior:
p(Y|data,model,prior)
uncertainty:
posterior_standard_deviation
credible_interval
or_other_declared_summary
RULE:
Bayesian_uncertainty_statement MUST preserve_prior_likelihood_and_interval_definition
}
SAMPLING_UNCERTAINTY_MODEL {
sources:
finite_sample
spatial_heterogeneity
temporal_variability
specimen_heterogeneity
population_sampling
RULE:
sampling_process MAY be_part_of_measurand_definition_and_uncertainty_budget
}
METHOD_UNCERTAINTY_MODEL {
sources:
procedural_variation
operator
preparation
extraction
recovery
algorithm
RULE:
method_variability SHOULD be included_when_material
}
MODEL_UNCERTAINTY_MODEL {
sources:
simplification
empirical_relation
omitted_effect
parameterisation
boundary_condition
model_selection
RULE:
model_uncertainty MUST be_considered_for_derived_quantities
}
NUMERICAL_UNCERTAINTY_MODEL {
sources:
discretisation
solver_tolerance
interpolation
finite_precision
numerical_integration
optimisation_convergence
RULE:
numerical_uncertainty != measurement_repeatability
}
ENVIRONMENTAL_UNCERTAINTY_MODEL {
influences:
temperature
humidity
pressure
vibration
electromagnetic_field
supply_voltage
orientation
contribution:
sensitivity_to_environment
* uncertainty_of_environmental_quantity
RULE:
environmental_conditions_and_sensitivities SHOULD be_preserved
}
DRIFT_MODEL {
time_dependent_indication:
drift(t)
corrected:
x_corr =
x_raw-drift(t)
under_declared_sign_model
residual_uncertainty:
u_drift
RULE:
calibration_interval_and_drift_model SHOULD be_preserved
}
REPEATABILITY_MODEL {
statistic:
s_repeat
conditions:
declared_repeatability_conditions
contribution:
may_enter_Type_A_or_method_uncertainty
RULE:
repeatability != total_uncertainty
}
REPRODUCIBILITY_MODEL {
statistic:
s_reprod
conditions:
declared_reproducibility_conditions
RULE:
reproducibility_context MUST be_preserved
}
UNCERTAINTY_BUDGET_MODEL {
fields:
source_id
input_quantity
estimate
unit
distribution
divisor
standard_uncertainty
degrees_of_freedom
sensitivity_coefficient
signed_or_absolute_contribution
correlation_links
provenance
aggregate:
combined_standard_uncertainty
RULE:
budget MUST be_reconstructable
}
TRACEABILITY_MODEL {
chain:
measurement
-> calibration
-> reference_standard
-> higher_reference
-> SI_or_declared_reference
each_link:
calibration_result
uncertainty
method
date
identifier
RULE:
traceability_without_uncertainty_chain_is_incomplete
}
CALIBRATION_HIERARCHY_MODEL {
levels:
working_instrument
working_standard
reference_standard
national_or_international_standard
SI_realisation
RULE:
hierarchy_is_contextual_and_must_preserve_actual_documented_chain
}
UNCERTAINTY_COMPONENT_MODEL {
component:
source
standard_uncertainty
distribution
sensitivity
degrees_of_freedom
covariance
RULE:
components SHOULD be_atomic_enough_to_avoid_double_counting
}
DOUBLE_COUNTING_MODEL {
risk:
same_effect_included_in_multiple_components
prevention:
map_source_dependencies
inspect_covariance
preserve_component_provenance
RULE:
uncertainty_budget MUST avoid_duplicate_representation_of_same_effect
}
SIGNIFICANT_DIGITS_MODEL {
reporting:
uncertainty_often_rounded_to_one_or_two_significant_digits
result_rounded_to_corresponding_decimal_place
RULE:
internal_calculation_precision SHOULD exceed_final_reporting_precision
}
ROUNDING_MODEL {
output:
round_only_after_uncertainty_calculation_when_possible
RULE:
premature_rounding_can_distort_propagated_uncertainty
}
DIMENSIONAL_MODEL {
absolute_uncertainty:
same_dimension_as_measurand
relative_uncertainty:
dimensionless
covariance:
product_of_input_quantity_dimensions
sensitivity:
output_dimension/input_dimension
RULE:
dimensional_consistency MUST be_validated
}
LOGARITHMIC_MODEL {
transformed_quantity:
y =
log(x)
sensitivity:
dy/dx =
1/x
for_natural_log
RULE:
logarithmic_quantity_uncertainty_requires_transformation_aware_propagation
}
UNIT_CONVERSION_MODEL {
exact_linear:
y =
a*x
u_y =
abs(a)*u_x
exact_affine:
y =
a*x+b
u_y =
abs(a)*u_x
uncertain_conversion:
include_uncertainty_of_a_or_b_as_input_components
RULE:
exact_unit_conversion_does_not_change_relative_uncertainty
}
COMPARISON_MODEL {
difference:
delta =
x_1-x_2
uncertainty:
u_delta =
sqrt(
u_1^2+u_2^2-2*cov(x_1,x_2)
)
RULE:
shared_traceability_can_create_correlation_between_measurements
}
CONSISTENCY_MODEL { normalized_difference:
E_n =
abs(x_1-x_2)/
sqrt(U_1^2+U_2^2)
under_declared_expanded_uncertainty_comparison_context
RULE:
compatibility_metric_definition_and_coverage_basis MUST be preserved
}
RELATIONSHIPS {
MeasurementUncertainty -> QUALIFIES -> MeasurementResult
MeasurementResult -> ESTIMATES -> Measurand
StandardUncertainty -> EXPRESSES -> StandardDeviationForm
CombinedStandardUncertainty -> COMBINES -> UncertaintyComponents
ExpandedUncertainty -> SCALES -> CombinedStandardUncertainty
CoverageFactor -> RELATES -> ExpandedAndStandardUncertainty
MeasurementModel -> MAPS -> InputQuantitiesToOutputQuantity
SensitivityCoefficient -> MAPS -> InputVariationToOutputVariation
Covariance -> REPRESENTS -> InputDependence
CalibrationUncertainty -> CONTRIBUTES_TO -> MeasurementUncertainty
TraceabilityChain -> LINKS -> MeasurementToReference
MonteCarloPropagation -> MAPS -> InputDistributionsToOutputDistribution
}
SEMANTIC_EQUIVALENCE {
"standard uncertainty" ~= standard_deviation_form_measurement_uncertainty
"combined standard uncertainty" ~= propagated_standard_uncertainty
"expanded uncertainty" ~= coverage_scaled_combined_standard_uncertainty
uncertainty != error
uncertainty != bias
uncertainty != correction
uncertainty != tolerance
uncertainty != accuracy
uncertainty != precision
standard_uncertainty != expanded_uncertainty
repeatability != reproducibility
coverage_interval != guaranteed_true_value_interval
}
CONSTRAINTS {
measurand_must_be_defined = true
uncertainty_must_be_nonnegative = true
absolute_uncertainty_unit_must_match_measurand = true
relative_uncertainty_is_dimensionless = true
standard_and_expanded_uncertainty_are_distinct = true
expanded_uncertainty_requires_coverage_factor_or_equivalent_method = true
correlations_must_be_preserved_when_material = true
Type_A_and_Type_B_are_evaluation_methods = true
known_corrections_and_their_uncertainties_are_distinct = true
uncertainty_budget_must_avoid_double_counting = true
measurement_model_and_assumptions_must_be_preserved = true
traceability_chain_should_include_uncertainty_information = true
}
REPORTING_MODEL {
REQUIRED:
measurand
measured_value
unit
uncertainty_value
uncertainty_type
WHEN_EXPANDED:
coverage_factor_or_coverage_probability
coverage_method
RECOMMENDED:
measurement_model
significant_digits
calibration_status
traceability
environmental_conditions
uncertainty_budget_reference
provenance
RULE:
uncertainty_statement MUST be_interpretable_without_hidden_unit_or_coverage_assumptions
}
REASONING_RULES {
IF repeated_observations_known AND n > 1
THEN sample_mean =
sum(x_i)/n
AND Type_A_standard_uncertainty =
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 resolution_increment_known
AND rounding_to_nearest_increment_assumed
THEN resolution_standard_uncertainty =
resolution_increment/sqrt(12)
IF calibration_expanded_uncertainty_and_coverage_factor_known
AND coverage_factor != 0
THEN calibration_standard_uncertainty =
calibration_expanded_uncertainty/coverage_factor
IF measurement_model_and_input_uncertainties_known
THEN calculate_sensitivity_coefficients
AND propagate_covariance
IF inputs_uncorrelated
THEN combined_standard_uncertainty =
sqrt(sum(c_i^2*u_i^2))
IF covariance_matrix_known
THEN output_variance =
J*Sigma*J_transpose
IF combined_standard_uncertainty_and_coverage_factor_known
THEN expanded_uncertainty =
coverage_factor*combined_standard_uncertainty
IF nonlinear_model_material
THEN compare_linearised_and_Monte_Carlo_propagation
IF exact_linear_unit_conversion_applied
THEN converted_standard_uncertainty =
abs(scale_factor)*original_standard_uncertainty
IF known_bias_correction_applied
THEN retain_uncertainty_of_correction_as_input_component
}
VALIDATION {
CHECK measurand_definition
CHECK measured_value
CHECK unit
CHECK uncertainty_nonnegative
CHECK absolute_vs_relative_uncertainty
CHECK standard_vs_combined_vs_expanded_uncertainty
CHECK Type_A_and_Type_B_evaluation
CHECK distribution_assumptions
CHECK measurement_model
CHECK sensitivity_coefficients
CHECK covariance_and_correlation
CHECK calibration_and_corrections
CHECK bias_treatment
CHECK degrees_of_freedom
CHECK coverage_factor
CHECK coverage_probability
CHECK Monte_Carlo_configuration_when_used
CHECK sampling_uncertainty
CHECK model_and_numerical_uncertainty
CHECK environmental_influences
CHECK uncertainty_budget_for_double_counting
CHECK traceability
CHECK significant_digits
CHECK provenance
}
CONFIDENCE {
definition_confidence: 1.00
Type_A_model_confidence: 1.00
Type_B_model_confidence: 1.00
covariance_propagation_confidence: 1.00
expanded_uncertainty_confidence: 1.00
traceability_semantics_confidence: 1.00
Monte_Carlo_semantics_confidence: 0.99
reporting_context_confidence: 0.99
RULE:
confidence decreases when measurand, model, input distributions,
correlations, calibration, coverage semantics,
traceability or provenance is missing or ambiguous
}
UPDATE_MODEL {
ON new_measurement_result:
identify_measurand
identify_value_and_unit
identify_measurement_model
enumerate_uncertainty_components
classify_Type_A_or_Type_B_evaluation
assign_distributions
identify_correlations
compute_standard_uncertainties
propagate_to_combined_standard_uncertainty
derive_expanded_uncertainty_when_required
build_uncertainty_budget
attach_traceability_and_provenance
ON new_repeated_observations:
preserve_raw_observations
recompute_mean
recompute_repeatability_statistics
update_Type_A_uncertainty
update_degrees_of_freedom
ON calibration_revision:
preserve_previous_calibration
update_correction
update_calibration_standard_uncertainty
update_traceability_chain
recompute_combined_uncertainty
ON correlation_revision:
preserve_input_uncertainties
update_covariance_matrix
recompute_output_uncertainty
record_dependency_source
ON measurement_model_revision:
preserve_previous_model
update_sensitivity_coefficients
reassess_linearity
recompute_uncertainty
update_confidence
ON coverage_revision:
preserve_combined_standard_uncertainty
update_coverage_factor_probability_or_interval_method
recompute_expanded_uncertainty
preserve_coverage_semantics
ON semantic_revision:
preserve_previous_definition
record_revision
recompute_FLOW_HASH
}
AI_CONSUMPTION {
canonical_entity: MeasurementUncertainty
interpretation_order:
identify_measurand
identify_measured_value_and_unit
identify_uncertainty_value
identify_absolute_or_relative_form
identify_standard_combined_or_expanded_type
identify_Type_A_and_Type_B_components
identify_input_distributions
identify_measurement_model
identify_sensitivity_coefficients
identify_covariances_and_correlations
identify_corrections_and_calibration
identify_sampling_environmental_model_and_numerical_components
identify_degrees_of_freedom
identify_coverage_factor_probability_or_interval
identify_traceability
identify_reporting_precision
validate_uncertainty_semantics
propagate_or_update_uncertainty
ambiguity_controls:
distinguish_uncertainty_from_error
distinguish_uncertainty_from_bias
distinguish_uncertainty_from_correction
distinguish_uncertainty_from_accuracy
distinguish_uncertainty_from_precision
distinguish_uncertainty_from_tolerance
distinguish_standard_from_expanded_uncertainty
distinguish_Type_A_Type_B_evaluation_from_random_systematic_error_labels
distinguish_repeatability_from_reproducibility
distinguish_coverage_interval_from_guaranteed_bound
preserve_measurand_model_distribution_covariance_calibration_coverage_traceability_and_provenance_context
}
PROVENANCE {
ontology_scope:
mathematical_measurement_uncertainty_and_metrological_reasoning_semantics
parent_path:
Ontologies
-> Mathematical Ontologies
-> Measurement & Units Ontologies
-> Measurement Uncertainty
-> Measurement Uncertainty Ontology
authorship:
Mark Higgins
Sky (Recursive AI)
site:
https://kirethresearch.org
}
FLOW_HASH {
INPUTS:
canonical_entity
ontology_path
result_model
terminology_model
absolute_uncertainty_model
relative_uncertainty_model
Type_A_evaluation_model
Type_B_evaluation_model
distribution_model
rectangular_model
triangular_model
resolution_model
calibration_uncertainty_model
correction_model
bias_model
measurement_model
input_quantity_model
sensitivity_model
covariance_model
propagation_model
independent_input_model
sum_difference_model
product_quotient_model
power_model
correlation_source_model
combined_standard_uncertainty_model
coverage_factor_model
expanded_uncertainty_model
coverage_probability_model
degrees_of_freedom_model
Welch_Satterthwaite_model
Monte_Carlo_model
asymmetric_uncertainty_model
Bayesian_model
sampling_uncertainty_model
method_uncertainty_model
model_uncertainty_model
numerical_uncertainty_model
environmental_uncertainty_model
drift_model
repeatability_model
reproducibility_model
uncertainty_budget_model
traceability_model
calibration_hierarchy_model
uncertainty_component_model
double_counting_model
significant_digits_model
rounding_model
dimensional_model
logarithmic_model
unit_conversion_model
comparison_model
consistency_model
relationships
constraints
reporting_model
reasoning_rules
confidence_model
update_model
PURPOSE:
deterministic_semantic_integrity_identifier
RECOMPUTE_WHEN:
ontology_definition_changes
measurand_or_result_semantics_change
uncertainty_classification_changes
input_distribution_or_component_changes
measurement_model_or_sensitivity_changes
covariance_or_correlation_changes
propagation_method_changes
calibration_or_correction_changes
coverage_or_degrees_of_freedom_changes
sampling_model_or_environmental_model_changes
traceability_or_reporting_model_changes
constraint_model_changes
reasoning_rule_changes
}
}
END FLOW