Instrument Drift Ontology
Instrumentation
Authors: MArk Anthony Higgins - Sky Recursive Ai
Revised Date: 16 September 2026
Audio Overview
# Instrument Drift Ontology — Flow Algebra
URL: https://kirethresearch.org/booksai-core/ontologies/mathematical-ontologies/measurement-units-ontologies/instrumentation/instrument-drift-ontology.html
FLOW InstrumentDriftOntology {
DOMAIN:
Mathematical Ontologies
-> Measurement & Units Ontologies
-> Instrumentation
-> Instrument Drift Ontology
PURPOSE:
Represent instrument drift as continuous or incremental change over time in an instrument's indication, calibration relation or metrological characteristics when the measurand and relevant influence quantities are held constant or appropriately modelled. The ontology models zero drift, span drift, sensitivity drift, gain drift, offset drift, long-term and short-term drift, deterministic and stochastic drift, thermal ageing and environmental effects, calibration history, stability, drift rate, correction, interpolation, extrapolation, uncertainty, time-series analysis, change detection, maintenance, provenance and machine-consumable reasoning while strictly distinguishing drift from random noise, hysteresis, repeatability, resolution, instantaneous error and true measurand change.
ENTITY InstrumentDrift {
TYPE: TimeDependentMetrologicalCharacteristic
DEFINITION:
continuous_or_incremental_change_over_time
in_indication_or_metrological_property
of_measuring_instrument
under_constant_or_modelled_measurand_and_influence_conditions
PROPERTIES:
depends_on_time
may_be_positive_or_negative
may_be_deterministic
may_be_stochastic
may_be_reversible
may_be_irreversible
may_affect_offset
may_affect_gain
may_affect_linearity
may_affect_dynamic_response
contributes_to_measurement_uncertainty
has_provenance
}
ENTITY DriftObservation {
TYPE: TimeIndexedMetrologicalObservation
COMPONENTS:
instrument_identifier
timestamp
reference_input
indication
environmental_conditions
calibration_state
uncertainty
}
ENTITY DriftRate {
TYPE: RateQuantity
SYMBOL:
dX/dt
DIMENSION:
quantity_dimension_per_time
}
TERMINOLOGY_MODEL {
terms:
drift
instrument_drift
zero_drift
offset_drift
span_drift
gain_drift
sensitivity_drift
baseline_drift
ageing
stability
noise
RULE:
drift MUST be interpreted_as_time_dependent_change
not_generic_measurement_variability
}
TIME_MODEL {
reference_epoch:
t_0
current_time:
t
elapsed:
delta_t =
t-t_0
RULE:
time_scale_and_epoch SHOULD be preserved_when_drift_correction_is_material
}
GENERAL_DRIFT_MODEL {
instrument_parameter:
theta(t)
drift:
D_theta(t) =
theta(t)-theta(t_0)
RULE:
reference_state_theta(t_0) MUST be identified
}
INDICATION_DRIFT_MODEL {
fixed_reference_input:
X_ref
indication:
Y(t)
indication_drift:
D_Y(t) =
Y(t)-Y(t_0)
RULE:
reference_input_stability_and_uncertainty MUST be considered
}
ZERO_DRIFT_MODEL {
reference:
zero_or_defined_baseline_input
offset:
b(t)
zero_drift:
D_0(t) =
b(t)-b(t_0)
RULE:
zero_drift_is_additive_response_change
}
OFFSET_DRIFT_MODEL {
calibration:
Y =
a*X+b(t)
RULE:
offset_drift_can_shift_entire_calibration_curve
}
SPAN_DRIFT_MODEL {
span_response:
S_span(t) =
Y_high(t)-Y_low(t)
drift:
D_span(t) =
S_span(t)-S_span(t_0)
RULE:
span_drift_requires_at_least_two_reference_levels
}
GAIN_DRIFT_MODEL {
gain:
a(t)
relative_gain_drift:
D_g,r =
[a(t)-a(t_0)]/a(t_0)
WHEN a(t_0) != 0
RULE:
gain_drift_is_multiplicative_effect
}
SENSITIVITY_DRIFT_MODEL {
sensitivity:
S(t) =
partial_Y/partial_X
drift:
D_S =
S(t)-S(t_0)
RULE:
sensitivity_drift MAY be_range_dependent_for_nonlinear_instruments
}
LINEARITY_DRIFT_MODEL {
calibration_curve:
f(X,t)
effect:
shape_changes_with_time
RULE:
zero_and_span_checks_alone_may_not_detect_linearity_drift
}
RANGE_DEPENDENT_DRIFT_MODEL {
drift:
D(X,t)
RULE:
drift_model MAY require_multiple_reference_points_across_range
}
SHORT_TERM_DRIFT_MODEL {
timescale:
seconds_to_hours_or_declared_interval
possible_causes:
warmup
thermal_equilibration
short_term_component_instability
battery_or_supply_change
RULE:
timescale MUST be declared
}
LONG_TERM_DRIFT_MODEL {
timescale:
days_months_years_or_declared_interval
possible_causes:
ageing
contamination
material relaxation
component ageing
radiation
mechanical creep
RULE:
long_term_drift_supports_calibration_interval_decisions
}
WARMUP_DRIFT_MODEL {
after_power_on:
instrument_parameters_approach_stable_state
possible:
exponential_settling
model:
D(t) =
A*exp(-t/tau)
under_declared_context
RULE:
measurements_before_stabilisation MAY require_correction_or_quality_flag
}
THERMAL_DRIFT_MODEL {
parameter:
theta(T)
first_order:
theta(T) =
theta_ref
+ alpha_T*(T-T_ref)
RULE:
temperature_change_is_influence_effect
and_apparent_time_drift_must_be_separated_when_temperature_history_known
}
HUMIDITY_DRIFT_MODEL {
mechanisms:
moisture_absorption
leakage
dimensional_change
chemical_change
RULE:
humidity_history MAY create_reversible_or_hysteretic_drift
}
PRESSURE_DRIFT_MODEL {
sensor:
response_may_change_with_ambient_pressure
RULE:
environmental_pressure_effect MUST be separated_from_intrinsic_time_drift_when_possible
}
SUPPLY_DRIFT_MODEL {
power_supply:
voltage_frequency_or_battery_state_changes
effect:
gain_offset_or_noise_change
RULE:
supply_monitoring_can_explain_apparent_drift
}
AGEING_MODEL {
parameter:
theta(age)
possible:
monotonic
logarithmic
exponential
random_walk_like
RULE:
ageing_model SHOULD be_supported_by_history_or_physics
}
MECHANICAL_CREEP_MODEL {
deformation:
evolves_under_load_or_stress
sensors:
load_cells
strain systems
pressure elements
RULE:
creep_and_drift_semantics MAY overlap_but_loading_history_must_be_preserved
}
CONTAMINATION_MODEL {
causes:
deposits
corrosion
optical_fouling
chemical_poisoning
biological_growth
effect:
baseline_sensitivity_or_selectivity_change
RULE:
cleaning_event MAY reset_or_change_drift_state
}
RADIATION_DRIFT_MODEL {
accumulated_dose:
may_change_electronic_optical_or_sensor_response
RULE:
dose_history SHOULD be preserved_when_material
}
REVERSIBLE_DRIFT_MODEL {
cause:
influence_quantity_change
response:
returns_toward_previous_state_when_condition_restored
RULE:
reversible_environmental_effect != irreversible_ageing
}
IRREVERSIBLE_DRIFT_MODEL {
cause:
ageing_damage_contamination_or_permanent_change
RULE:
irreversible_drift_may_require_recalibration_adjustment_or_replacement
}
DETERMINISTIC_DRIFT_MODEL {
drift:
represented_by_predictable_function
examples:
linear
polynomial
exponential
temperature_compensated
RULE:
deterministic_model_requires_residual_analysis
}
LINEAR_DRIFT_MODEL {
parameter:
theta(t) =
theta_0+d*(t-t_0)
drift_rate:
d
correction:
theta_corrected =
theta_observed-d*(t-t_0)
under_declared_sign_and_model
RULE:
linear_model_validity_interval MUST be defined
}
POLYNOMIAL_DRIFT_MODEL {
theta(t) =
a_0+a_1*t+a_2*t^2+...+a_n*t^n
RULE:
high_order_extrapolation_is_unstable
}
EXPONENTIAL_DRIFT_MODEL {
theta(t) =
theta_inf
+ A*exp(-t/tau)
possible:
warmup
relaxation
ageing_process
RULE:
parameter_identifiability_and_validity MUST be checked
}
PIECEWISE_DRIFT_MODEL {
segments:
different_drift_models_between_events
boundaries:
calibration
maintenance
adjustment
firmware_change
relocation
RULE:
event_boundaries MUST be preserved
}
STOCHASTIC_DRIFT_MODEL {
parameter:
theta(t)
possible:
random_walk
flicker
autoregressive_process
state_space_process
RULE:
stochastic_drift_requires_probabilistic_time_series_model
}
RANDOM_WALK_MODEL {
theta_k =
theta_(k-1)+w_k
process_noise:
w_k
variance:
grows_with_elapsed_steps_under_simple_model
RULE:
random_walk_drift_is_nonstationary
}
AR_MODEL {
example:
D_t =
phi*D_(t-1)+epsilon_t
RULE:
autoregressive_model_requires_sampling_interval_and_stationarity_context
}
STATE_SPACE_MODEL {
state:
drift_parameter
transition:
theta_k =
F*theta_(k-1)+w_k
observation:
y_k =
H*theta_k+v_k
RULE:
process_and_measurement_noise_covariances MUST be preserved
}
KALMAN_MODEL {
use:
recursively_estimate_time_varying_drift_state
REQUIRE:
state_model
process_noise
measurement_noise
initial_covariance
RULE:
filter_estimate_is_model_dependent
}
NOISE_DISTINCTION_MODEL {
noise:
short_term_stochastic_variation
drift:
slower_or_persistent_time_dependent_change
RULE:
frequency_or_timescale_boundary_is_context_dependent
}
REPEATABILITY_DISTINCTION_MODEL {
repeatability:
dispersion_under_repeatability_conditions
drift:
time_dependent_change_in_mean_or_parameter
RULE:
drift_can_inflate_repeatability_if_study_duration_is_long
}
HYSTERESIS_DISTINCTION_MODEL {
hysteresis:
response_depends_on_input_history_and_direction
drift:
response_changes_with_time
RULE:
hysteresis != drift
though_both_can_coexist
}
MEASURAND_CHANGE_DISTINCTION_MODEL {
observed_output_change:
may_be_due_to_true_input_change_or_instrument_drift
RULE:
drift_inference_requires_stable_reference_or_independent_measurand_evidence
}
ERROR_DISTINCTION_MODEL {
drift:
source_of_time_dependent_systematic_or_stochastic_measurement_error
RULE:
drift != instantaneous_measurement_error
}
STABILITY_MODEL {
stability:
ability_to_maintain_metrological_characteristics_over_time
relationship:
greater_drift_generally_implies_lower_stability
RULE:
stability_metric_and_timescale MUST be defined
}
DRIFT_RATE_MODEL {
finite_difference:
d approximately_equals
[theta(t_2)-theta(t_1)]/(t_2-t_1)
instantaneous:
dtheta/dt
RULE:
rate_unit MUST include_time_dimension
}
RELATIVE_DRIFT_MODEL {
D_rel =
[theta(t)-theta(t_0)]/abs(theta(t_0))
WHEN theta(t_0) != 0
RULE:
relative_drift_is_dimensionless
}
PERCENT_DRIFT_MODEL {
percent =
100*D_rel
RULE:
percent_drift_requires_nonzero_reference_value
}
DRIFT_PER_TIME_MODEL {
examples:
microvolt_per_day
ppm_per_year
degree_per_hour
RULE:
unit_and_reference_quantity MUST be explicit
}
REFERENCE_CHECK_MODEL {
stable_reference:
X_ref
repeated_indication:
Y(t)
purpose:
estimate_instrument_drift
RULE:
reference_standard_uncertainty_and_drift MUST be included
}
CHECK_STANDARD_MODEL {
independent_standard:
monitored_over_time
RULE:
check_standard_must_be_more_stable_or_separately_characterised_to_identify_instrument_drift
}
CALIBRATION_HISTORY_MODEL {
events:
calibration_i
fields:
date
offset
gain
linearity
uncertainty
environment
RULE:
historical_calibration_parameters SHOULD be retained_not_overwritten
}
AS_FOUND_MODEL {
before_adjustment:
calibration_state
drift_evidence:
difference_from_previous_as_left_state
RULE:
as_found_data_are_critical_for_drift_analysis
}
AS_LEFT_MODEL {
after_calibration_or_adjustment:
reference_state
RULE:
as_left_state_can_define_new_drift_epoch
}
CALIBRATION_INTERVAL_MODEL {
interval:
time_between_calibrations
optimisation:
balance_drift_risk
required_uncertainty
cost
instrument_stability
RULE:
interval_should_be_updated_from_drift_history_when_evidence_supports
}
INTERMEDIATE_CHECK_MODEL {
between_calibrations:
periodic_reference_measurements
purpose:
detect_unexpected_drift
RULE:
check_frequency SHOULD match_drift_risk_and_measurement_criticality
}
CHANGE_POINT_MODEL {
time_series:
detect_abrupt_shift
possible_causes:
damage
adjustment
component_failure
relocation
contamination
RULE:
abrupt_change_should_not_be_forced_into_smooth_drift_model
}
CONTROL_CHART_MODEL {
reference_measurements:
monitored_over_time
possible:
Shewhart
EWMA
CUSUM
RULE:
control_limits_are_monitoring_thresholds_not_measurement_uncertainty
}
EWMA_MODEL {
z_t =
lambda*x_t
+(1-lambda)*z_(t-1)
RULE:
smoothing_parameter MUST be preserved
}
CUSUM_MODEL {
cumulative_deviation:
detects_small_persistent_shifts
RULE:
reference_value_and_decision_interval MUST be defined
}
ALLAN_VARIANCE_MODEL {
purpose:
characterise_noise_and_drift_over_averaging_times
useful_for:
clocks
inertial_sensors
frequency_standards
precision_instruments
RULE:
Allan_deviation_and_classical_standard_deviation_are_distinct
}
TREND_ESTIMATION_MODEL {
data:
theta_i,t_i
methods:
least_squares
robust_regression
Bayesian
state_space
RULE:
uncertainty_and_autocorrelation MUST be considered
}
ROBUST_DRIFT_MODEL {
methods:
Theil_Sen
robust_regression
median_slope
RULE:
robust_method_definition MUST be preserved
}
OUTLIER_MODEL {
unusual_reference_check:
may_be_gross_error_or_real_change
RULE:
outlier_removal_requires_documented_criterion
and_original_record_must_be_preserved
}
AUTOCORRELATION_MODEL {
residuals:
may_be_time_correlated
RULE:
trend_uncertainty_must_account_for_autocorrelation_when_material
}
INTERPOLATION_MODEL {
between_calibration_points:
estimate_drift_correction
RULE:
interpolation_uncertainty MUST be included
}
EXTRAPOLATION_MODEL {
after_last_calibration:
predict_future_drift
RULE:
extrapolation_uncertainty_increases_with_horizon_and_model_uncertainty
}
CORRECTION_MODEL {
observed:
X_obs
estimated_drift:
D_hat(t)
corrected:
X_corr =
X_obs-D_hat(t)
under_declared_sign_model
RULE:
correction_uncertainty MUST be propagated
}
UNCERTAINTY_MODEL {
components:
drift_model_parameter_uncertainty
residual_drift
reference_standard
interpolation
extrapolation
environmental_separation
time_uncertainty
change_point_uncertainty
RULE:
drift_correction_does_not_eliminate_drift_uncertainty
}
DRIFT_UNCERTAINTY_GROWTH_MODEL {
elapsed_time:
delta_t
uncertainty:
may_increase_with_delta_t
model:
depends_on_deterministic_or_stochastic_drift_process
RULE:
growth_model MUST be justified_by_history_or_physics
}
WORST_CASE_MODEL {
specified_drift_rate_limit:
abs(d) <= d_max
bound:
abs(D) <= d_max*delta_t
RULE:
worst_case_bound != standard_uncertainty
}
MANUFACTURER_SPECIFICATION_MODEL {
drift_spec:
may_be_given_per_time_temperature_or_usage
RULE:
specification_conditions_and_statistical_basis MUST be interpreted_before_uncertainty_use
}
MULTIPARAMETER_DRIFT_MODEL {
calibration_parameters:
theta =
[offset,gain,nonlinearity,...]
covariance:
Sigma_theta(t)
RULE:
parameter_correlations SHOULD be preserved
}
MULTICHANNEL_DRIFT_MODEL {
channels:
may_share_reference_power_supply_ADC_or_environment
RULE:
common_drift_sources SHOULD generate_covariance
}
MAINTENANCE_MODEL {
events:
cleaning
repair
adjustment
component_replacement
firmware_update
RULE:
maintenance_event MAY reset_or_change_drift_process
}
DAMAGE_MODEL {
event:
overload
shock
contamination
thermal_excursion
RULE:
suspected_damage SHOULD trigger_change_point_review_and_recalibration
}
FIRMWARE_MODEL {
digital_instrument:
correction_or_calibration_algorithm_may_change
RULE:
firmware_change MUST be distinguished_from_physical_sensor_drift
}
SOFTWARE_MODEL {
processing:
may_apply_time_dependent_correction
RULE:
software_version_and_drift_model_version MUST be preserved
}
METADATA_MODEL {
fields:
instrument_identifier
drift_parameter
reference_epoch
timestamps
reference_input
indications
environmental_conditions
calibration_identifiers
as_found_state
as_left_state
drift_model
drift_rate
model_parameters
covariance
correction
uncertainty
maintenance_events
quality_flags
provenance
RULE:
drift_metadata MUST support_reconstruction_of_time_dependent_state
}
RELATIONSHIPS {
InstrumentDrift -> QUALIFIES -> MeasuringInstrument
InstrumentDrift -> CHANGES -> CalibrationRelation
ZeroDrift -> CHANGES -> Offset
GainDrift -> CHANGES -> Sensitivity
DriftObservation -> REFERENCES -> CalibrationStandard
CalibrationHistory -> SUPPORTS -> DriftModel
DriftModel -> PRODUCES -> DriftCorrection
DriftCorrection -> CONTRIBUTES_TO -> CorrectedMeasurement
DriftUncertainty -> CONTRIBUTES_TO -> MeasurementUncertainty
MaintenanceEvent -> MAY_CHANGE -> DriftState
EnvironmentalInfluence -> MAY_MIMIC_OR_CAUSE -> InstrumentDrift
}
SEMANTIC_EQUIVALENCE {
"instrument drift" ~= time_dependent_change_in_instrument_metrological_characteristic
"zero drift" ~= time_dependent_offset_change WHEN zero_reference_context
"gain drift" ~= time_dependent_scale_factor_change
drift != random_noise
drift != hysteresis
drift != repeatability
drift != resolution
drift != instantaneous_measurement_error
drift != true_measurand_change
drift_correction != drift_uncertainty
stability != drift
}
CONSTRAINTS {
instrument_identity_must_be_defined = true
drift_parameter_must_be_defined = true
reference_epoch_must_be_defined = true
time_coordinates_must_be_preserved = true
reference_input_or_independent_measurand_evidence_required_for_drift_inference = true
environmental_influences_should_be_separated_when_material = true
calibration_history_should_not_be_overwritten = true
drift_correction_uncertainty_must_be_preserved = true
abrupt_changes_should_not_be_forced_into_smooth_model = true
drift_and_noise_are_not_semantically_equivalent = true
historical_measurements_must_use_time_valid_drift_state = true
}
MEASUREMENT_MODEL {
RECORD:
instrument_identifier
parameter_identifier
reference_epoch
observation_timestamp
reference_input
reference_input_uncertainty
indication
indication_unit
offset
gain
sensitivity
environmental_conditions
calibration_identifier
as_found_state
as_left_state
drift_value
drift_rate
relative_drift
drift_model
model_parameters
parameter_covariance
residuals
correction
correction_uncertainty
interpolation_or_extrapolation_status
maintenance_events
firmware_version
quality_flags
provenance
RULE:
drift_estimate MUST remain_linked_to_reference_data_time_model_and_instrument_state
}
REASONING_RULES {
IF parameter_values_at_two_times_known
AND t_2 != t_1
THEN finite_drift_rate =
(theta_2-theta_1)/(t_2-t_1)
IF linear_drift_rate_and_reference_epoch_known
THEN estimated_drift =
drift_rate*(t-reference_epoch)
IF estimated_drift_known
THEN corrected_measurement =
observed_measurement-estimated_drift
under_declared_sign_model
IF offset_changes_while_gain_stable
THEN classify_as_offset_or_zero_drift
IF span_or_sensitivity_changes
THEN classify_as_gain_or_sensitivity_drift
IF environmental_quantity_changes
AND known_cross_sensitivity_explains_observed_shift
THEN classify_effect_as_environmental_influence
rather_than_intrinsic_drift_when_supported
IF reference_standard_itself_has_drift
THEN separate_reference_and_instrument_components
using_independent_traceability_or_model
IF abrupt_change_detected
THEN create_new_drift_segment
and_review_damage_adjustment_or_maintenance_events
IF extrapolating_beyond_last_calibration
THEN increase_uncertainty_according_to_declared_drift_model
IF common_drift_source_shared_across_channels
THEN preserve_cross_channel_covariance
IF maintenance_adjustment_or_firmware_change_occurs
THEN create_new_instrument_state_epoch
and_reassess_drift_model
}
VALIDATION {
CHECK instrument_identity
CHECK drift_parameter
CHECK reference_epoch
CHECK timestamps
CHECK reference_input_and_uncertainty
CHECK environmental_conditions
CHECK zero_offset_gain_and_sensitivity
CHECK drift_rate_units
CHECK deterministic_vs_stochastic_model
CHECK residuals_and_autocorrelation
CHECK warmup_and_short_term_drift
CHECK long_term_ageing
CHECK calibration_history
CHECK as_found_and_as_left_states
CHECK change_points
CHECK interpolation_vs_extrapolation
CHECK correction_sign
CHECK correction_uncertainty
CHECK common_channel_covariance
CHECK maintenance_damage_and_firmware_events
CHECK drift_vs_noise
CHECK drift_vs_measurand_change
CHECK provenance
}
CONFIDENCE {
definition_confidence: 1.00
zero_gain_drift_semantics_confidence: 1.00
drift_noise_distinction_confidence: 1.00
calibration_history_confidence: 1.00
deterministic_drift_model_confidence: 0.99
stochastic_drift_model_confidence: 0.98
environmental_separation_confidence: 0.98
uncertainty_integration_confidence: 0.99
RULE:
confidence decreases when reference input, timestamps, environment,
calibration history, drift model, change events,
correction uncertainty or provenance is missing or ambiguous
}
UPDATE_MODEL {
ON new_reference_check:
identify_instrument_and_parameter
append_timestamp_reference_and_indication
attach_environmental_conditions
compute_difference_from_reference_state
update_drift_model
update_uncertainty
preserve_provenance
ON new_calibration:
preserve_previous_calibration
record_as_found_state
record_as_left_state
establish_new_drift_epoch_when_appropriate
update_drift_history_and_interval_model
ON environmental_revision:
preserve_raw_drift_observations
update_temperature_humidity_pressure_or_supply_compensation
recompute_intrinsic_drift_estimate
update_uncertainty
ON change_point_detection:
preserve_previous_segment
create_new_segment
link_damage_maintenance_adjustment_or_configuration_event
refit_drift_models
ON maintenance_or_adjustment:
preserve_pre_event_state
record_event
start_new_instrument_state
require_reference_check_or_recalibration
reassess_drift_model
ON drift_model_revision:
preserve_previous_model
update_linear_polynomial_exponential_or_stochastic_form
recompute_corrections_and_uncertainties
record_model_selection_reason
ON semantic_revision:
preserve_previous_definition
record_revision
recompute_FLOW_HASH
}
AI_CONSUMPTION {
canonical_entity: InstrumentDrift
canonical_role: TimeDependentMetrologicalCharacteristic
interpretation_order:
identify_instrument
identify_drifting_parameter
identify_reference_epoch
identify_reference_input_or_stable_measurand
identify_time_series_of_indications_or_parameters
identify_environmental_influences
identify_zero_gain_span_or_linearity_drift
identify_short_or_long_term_timescale
identify_deterministic_or_stochastic_model
identify_calibration_as_found_and_as_left_history
identify_change_points_and_events
identify_drift_rate_and_correction
identify_interpolation_or_extrapolation
identify_uncertainty_and_covariance
identify_provenance
validate_drift_semantics
infer_time_dependent_instrument_state
ambiguity_controls:
distinguish_drift_from_random_noise
distinguish_drift_from_hysteresis
distinguish_drift_from_repeatability
distinguish_drift_from_resolution
distinguish_drift_from_instantaneous_measurement_error
distinguish_drift_from_true_measurand_change
distinguish_environmental_influence_from_intrinsic_time_drift
distinguish_drift_correction_from_drift_uncertainty
distinguish_stability_from_drift
preserve_instrument_parameter_time_reference_environment_calibration_event_model_and_provenance_context
}
PROVENANCE {
ontology_scope:
mathematical_measurement_units_instrumentation_and_instrument_drift_semantics
parent_path:
Ontologies
-> Mathematical Ontologies
-> Measurement & Units Ontologies
-> Instrumentation
-> Instrument Drift Ontology
authorship:
Mark Higgins
Sky (Recursive AI)
site:
https://kirethresearch.org
}
FLOW_HASH {
INPUTS:
canonical_entity
ontology_path
terminology_model
time_model
general_drift_model
indication_drift_model
zero_drift_model
offset_drift_model
span_drift_model
gain_drift_model
sensitivity_drift_model
linearity_drift_model
range_dependent_drift_model
short_term_drift_model
long_term_drift_model
warmup_drift_model
thermal_drift_model
humidity_drift_model
pressure_drift_model
supply_drift_model
ageing_model
mechanical_creep_model
contamination_model
radiation_drift_model
reversible_drift_model
irreversible_drift_model
deterministic_drift_model
linear_drift_model
polynomial_drift_model
exponential_drift_model
piecewise_drift_model
stochastic_drift_model
random_walk_model
AR_model
state_space_model
Kalman_model
noise_distinction_model
repeatability_distinction_model
hysteresis_distinction_model
measurand_change_distinction_model
error_distinction_model
stability_model
drift_rate_model
relative_drift_model
percent_drift_model
drift_per_time_model
reference_check_model
check_standard_model
calibration_history_model
as_found_model
as_left_model
calibration_interval_model
intermediate_check_model
change_point_model
control_chart_model
EWMA_model
CUSUM_model
Allan_variance_model
trend_estimation_model
robust_drift_model
outlier_model
autocorrelation_model
interpolation_model
extrapolation_model
correction_model
uncertainty_model
drift_uncertainty_growth_model
worst_case_model
manufacturer_specification_model
multiparameter_drift_model
multichannel_drift_model
maintenance_model
damage_model
firmware_model
software_model
metadata_model
relationships
constraints
measurement_model
reasoning_rules
confidence_model
update_model
PURPOSE:
deterministic_semantic_integrity_identifier
RECOMPUTE_WHEN:
ontology_definition_changes
drift_parameter_or_reference_epoch_changes
zero_gain_span_or_linearity_drift_model_changes
environmental_or_ageing_model_changes
deterministic_or_stochastic_drift_model_changes
calibration_history_or_reference_check_changes
change_point_or_maintenance_event_changes
correction_interpolation_or_extrapolation_changes
covariance_or_uncertainty_model_changes
measurement_record_changes
constraint_model_changes
reasoning_rule_changes
}
}
END FLOW