Sampling Ontology
Instrumentation
Authors: MArk Anthony Higgins - Sky Recursive Ai
Revised Date: 16 September 2026
Audio Overview
# Sampling Ontology — Flow Algebra
URL: https://kirethresearch.org/booksai-core/ontologies/mathematical-ontologies/measurement-units-ontologies/instrumentation/sampling-ontology.html
FLOW SamplingOntology {
DOMAIN:
Mathematical Ontologies
-> Measurement & Units Ontologies
-> Instrumentation
-> Sampling Ontology
PURPOSE:
Represent sampling as the process of selecting, observing or acquiring discrete values, events, specimens or subsets from a continuous signal, temporal process, spatial field, population or material system. The ontology models temporal sampling, spatial sampling, statistical sampling, physical specimen sampling, sampling frequency and interval, Nyquist-Shannon conditions, aliasing, sample-and-hold behaviour, aperture, jitter, multiplexing, synchronous and asynchronous sampling, random and stratified sampling, representativeness, sampling bias, sample size, uncertainty, provenance, metadata and machine-consumable reasoning while strictly distinguishing sampling from measurement, quantisation, interpolation, averaging and complete population observation.
ENTITY Sampling {
TYPE: ObservationSelectionProcess
DEFINITION:
process_of_selecting_or_acquiring_discrete_observations
from_a_signal_process_field_population_or_material
according_to_declared_sampling_design
PROPERTIES:
has_sampling_domain
has_sampling_unit
has_sampling_design
has_sampling_interval_or_selection_rule
has_sample_size
may_be_temporal
may_be_spatial
may_be_statistical
may_be_physical
may_introduce_sampling_uncertainty
may_introduce_sampling_bias
has_provenance
}
ENTITY Sample {
TYPE: SelectedObservationOrSubset
COMPONENTS:
sample_identifier
source_population_or_process
sampling_time_or_location
observed_value_or_specimen
selection_probability_when_relevant
metadata
provenance
}
ENTITY SamplingFrame {
TYPE: CandidateUnitSet
DEFINITION:
operational_representation_of_population_or_domain
from_which_sampling_units_are_selected
RULE:
frame_coverage_errors MAY create_sampling_bias
}
ENTITY SamplingUnit {
TYPE: SelectableElement
POSSIBLE:
time_point
time_interval
spatial_point
pixel
specimen
person
object
event
batch
volume
area
}
SAMPLING_DOMAIN_MODEL {
possible:
temporal_signal
spatial_field
population
material
event_stream
image
spectrum
distributed_sensor_network
RULE:
sampling_semantics MUST identify_domain
}
TEMPORAL_SAMPLING_MODEL {
continuous_signal:
x(t)
uniform_samples:
x[n] =
x(n*T_s)
sampling_interval:
T_s
sampling_frequency:
f_s =
1/T_s
WHEN T_s > 0
RULE:
time_reference_and_sample_timestamps SHOULD be preserved
}
UNIFORM_SAMPLING_MODEL {
sample_times:
t_n =
t_0+n*T_s
RULE:
clock_jitter_and_missing_samples_create_deviation_from_ideal_uniform_grid
}
NONUNIFORM_SAMPLING_MODEL {
sample_times:
t_n
not_equally_spaced
REQUIRE:
explicit_timestamps
RULE:
algorithms_assuming_uniform_sampling MUST NOT be_applied_without_resampling_or_nonuniform_method
}
SAMPLING_FREQUENCY_MODEL {
symbol:
f_s
dimension:
T^-1
SI_unit:
Hz
inverse:
T_s =
1/f_s
WHEN f_s > 0
}
NYQUIST_FREQUENCY_MODEL {
f_N =
f_s/2
RULE:
Nyquist_frequency_is_property_of_sampling_rate
not_necessarily_highest_recoverable_signal_frequency_in_practical_system
}
NYQUIST_SHANNON_MODEL {
ideal_condition:
bandlimited_signal_with_max_frequency_B
sufficient_uniform_sampling:
f_s > 2*B
reconstruction:
ideal_sinc_interpolation
under_theorem_conditions
RULE:
practical_sampling_requires_anti_alias_transition_margin
}
ALIASING_MODEL {
continuous_frequency:
f
sampled_equivalence:
frequencies_differing_by_integer_multiples_of_f_s
may_map_to_same_discrete_frequency
RULE:
aliasing_is_information_loss_and_cannot_generally_be_reversed_after_sampling
}
ANTI_ALIAS_MODEL {
analogue_filter:
attenuates_frequencies_that_would_alias
REQUIRE:
passband
stopband
transition_band
sampling_frequency
RULE:
anti_alias_filter MUST precede_or_be_intrinsic_to_rate_reduction
}
BANDPASS_SAMPLING_MODEL {
narrow_band_signal:
may_be_sampled_below_twice_highest_absolute_frequency
under_specific_bandpass_sampling_conditions
RULE:
valid_sampling_rate_ranges_depend_on_band_edges
and_alias_mapping_must_be_unambiguous
}
COMPLEX_SAMPLING_MODEL {
analytic_or_IQ_signal:
complex_samples
RULE:
Nyquist_interpretation_differs_from_real_signal_sampling
and_representation_must_be_declared
}
SAMPLE_HOLD_MODEL {
sample_and_hold:
captures_signal_over_finite_aperture
then_holds_value_for_conversion
parameters:
acquisition_time
aperture_time
hold_time
droop
RULE:
sample_is_not_mathematically_instantaneous_in_real_hardware
}
APERTURE_MODEL {
aperture_time:
finite_integration_or_capture_window
effect:
frequency_response_may_include_sinc_like_attenuation
RULE:
aperture_definition SHOULD be preserved_for_high_frequency_measurement
}
JITTER_MODEL {
actual_time:
t_n =
ideal_t_n+delta_t_n
timing_error:
delta_t_n
RULE:
sampling_jitter_converts_signal_slope_into_amplitude_error
}
JITTER_ERROR_MODEL {
first_order:
delta_x approximately_equals
dx/dt * delta_t
sinusoidal_jitter_limited_SNR:
SNR_dB approximately_equals
-20*log10(2*pi*f_in*sigma_t)
RULE:
approximation_requires_small_timing_error
}
CLOCK_MODEL {
sampling_clock:
defines_sample_times
characteristics:
frequency_accuracy
stability
drift
phase_noise
jitter
RULE:
clock_reference MUST be preserved_when_timing_accuracy_is_material
}
SYNCHRONOUS_SAMPLING_MODEL {
multiple_channels_or_systems:
share_clock_or_known_phase_relation
RULE:
synchronous_does_not_imply_zero_channel_skew
}
ASYNCHRONOUS_SAMPLING_MODEL {
channels:
independent_clocks_or_uncoordinated_times
REQUIRE:
timestamps
clock_models
alignment_method
RULE:
asynchronous_data_require_time_alignment_before_direct_phase_comparison
}
MULTIPLEXED_SAMPLING_MODEL {
channels:
sampled_sequentially
channel_time:
t_(n,c)
RULE:
channel_samples_in_same_scan_are_not_simultaneous
}
SIMULTANEOUS_SAMPLING_MODEL {
channels:
independent_sample_hold_or_ADC_paths
RULE:
residual_skew_and_clock_distribution_delay MAY remain
}
CHANNEL_SKEW_MODEL {
channel_delay:
delta_t_c
sinusoidal_phase_shift:
delta_phi =
2*pi*f*delta_t_c
RULE:
skew_correction_requires_known_time_offset
}
OVERSAMPLING_MODEL {
sampling_frequency:
greater_than_minimum_required_for_signal_band
possible_benefits:
relaxed_filter_transition
digital_decimation
noise_reduction_under_specific_noise_models
RULE:
oversampling_does_not_remove_systematic_measurement_error
}
UNDERSAMPLING_MODEL {
sampling_frequency:
below_required_rate_for_unambiguous_baseband_reconstruction
RULE:
undersampling_causes_aliasing_unless_valid_bandpass_sampling_design_applies
}
DECIMATION_MODEL {
input_rate:
f_s
factor:
M
output_rate:
f_s_out =
f_s/M
REQUIRE:
low_pass_or_band_appropriate_filter
RULE:
decimation_without_anti_alias_filter_can_destroy_information
}
INTERPOLATION_MODEL {
input_samples:
x[n]
output:
estimates_between_original_sample_times
RULE:
interpolation_does_not_create_new_measured_information
}
RESAMPLING_MODEL {
original:
sample_times_or_rate
target:
new_times_or_rate
process:
reconstruction_or_interpolation_plus_sampling
RULE:
resampling_filter_and_timestamp_mapping MUST be preserved
}
DOWNsampling_MODEL {
reduce_rate:
keep_subset_or_resample
RULE:
downsampling_requires_anti_alias_control
}
UPSAMPLING_MODEL {
increase_discrete_rate:
insert_intermediate_samples_then_filter
RULE:
upsampling_does_not_increase_original_signal_information_content
}
INTEGRATING_SAMPLE_MODEL {
sample:
average_or_integral_over_interval
example:
x_n =
(1/T_int)*integral_window(x(t)dt)
RULE:
integration_time_is_part_of_sampling_kernel
}
SAMPLING_KERNEL_MODEL {
observed_sample:
weighted_integral_of_signal_around_sample_location_or_time
kernel:
h_s
RULE:
real_sampling_may_be_spatiotemporal_convolution_then_discretisation
}
SPATIAL_SAMPLING_MODEL {
field:
X(r)
samples:
X(r_i)
patterns:
regular_grid
random
stratified
adaptive
transect
RULE:
spatial_coordinates_and_coordinate_reference_system MUST be preserved
}
SPATIAL_NYQUIST_MODEL {
spatial_frequency:
cycles_per_unit_distance
regular_spacing:
delta_x
spatial_Nyquist:
1/(2*delta_x)
RULE:
point_spread_function_and_sensor_footprint_also_limit_spatial_resolution
}
PIXEL_SAMPLING_MODEL {
image:
sampled_on_pixel_grid
pixel_pitch:
delta_x
delta_y
RULE:
pixel_pitch != optical_resolution
}
SENSOR_FOOTPRINT_MODEL {
sample:
spatial_average_over_footprint
RULE:
footprint_size_and_shape MUST be distinguished_from_sample_spacing
}
REMOTE_SENSING_MODEL {
sampling:
ground_sample_distance
swath
revisit_time
integration_time
RULE:
spatial_temporal_and_spectral_sampling_are_distinct_dimensions
}
SPECTRAL_SAMPLING_MODEL {
spectral_coordinate:
wavelength
frequency
energy
mass_to_charge
sample_spacing:
delta_lambda_or_equivalent
RULE:
spectral_sample_spacing != spectral_resolution
}
EVENT_SAMPLING_MODEL {
events:
irregular_occurrences
record:
event_time
type
magnitude
RULE:
event_stream_is_not_uniformly_sampled_signal
}
TRIGGERED_SAMPLING_MODEL {
trigger:
defines_acquisition_start_or_alignment
possible:
level
edge
event
external
software
RULE:
trigger_latency_and_jitter SHOULD be preserved
}
ADAPTIVE_SAMPLING_MODEL {
sampling_design:
changes_based_on_observed_data_or_state
examples:
higher_rate_near_event
denser_spatial_sampling_near_gradient
RULE:
adaptation_rule MUST be preserved_to_avoid_selection_bias
}
RANDOM_SAMPLING_MODEL {
selection:
sampling_units_selected_by_random_mechanism
REQUIRE:
selection_probability_or_design
RULE:
random_sampling != arbitrary_or_convenience_sampling
}
SIMPLE_RANDOM_SAMPLE_MODEL {
population:
N_units
sample:
n_units
equal_probability:
under_declared_without_or_with_replacement_design
RULE:
replacement_status MUST be declared
}
SYSTEMATIC_STATISTICAL_SAMPLING_MODEL {
population_order:
choose_random_start
then_every_kth_unit
RULE:
periodicity_in_population_order_can_bias_systematic_sample
}
STRATIFIED_SAMPLING_MODEL {
population:
divided_into_strata
sample:
selected_within_each_stratum
allocation:
proportional
equal
optimal
custom
RULE:
stratum_weights MUST be preserved
}
CLUSTER_SAMPLING_MODEL {
population:
grouped_into_clusters
process:
sample_clusters
then_all_or_subset_of_units
RULE:
intracluster_correlation_changes_variance
}
MULTISTAGE_SAMPLING_MODEL {
stages:
hierarchical_selection
RULE:
inclusion_probabilities_and_stage_design MUST be preserved
}
CONVENIENCE_SAMPLING_MODEL {
selection:
based_on_availability
RULE:
convenience_sample_should_not_be_labelled_probability_sample
}
PURPOSIVE_SAMPLING_MODEL {
selection:
based_on_declared_expert_or_target_criteria
RULE:
inferential_scope_requires_explicit_limit
}
REPRESENTATIVENESS_MODEL {
sample:
adequately_reflects_target_population_or_field_for_intended_inference
RULE:
representativeness_is_not_guaranteed_by_sample_size_alone
}
SAMPLING_BIAS_MODEL {
causes:
incomplete_frame
nonresponse
convenience_selection
spatial_access
temporal_window
adaptive_rule
specimen_loss
RULE:
repeated_measurement_of_biased_sample_does_not_remove_sampling_bias
}
COVERAGE_ERROR_MODEL {
sampling_frame:
misses_or_duplicates_target_units
RULE:
frame_error MUST be distinguished_from_measurement_error
}
NONRESPONSE_MODEL {
selected_unit:
no_observation
mechanisms:
random
conditionally_random
nonrandom
RULE:
nonresponse_adjustment_requires_model_or_weighting
}
SAMPLE_SIZE_MODEL {
symbol:
n
RULE:
larger_n_reduces_some_sampling_variance
but_does_not_automatically_reduce_bias
}
FINITE_POPULATION_MODEL {
population_size:
N
sampling_fraction:
n/N
finite_population_correction:
sqrt((N-n)/(N-1))
under_simple_random_sampling_without_replacement
RULE:
correction_relevant_when_sampling_fraction_not_negligible
}
SAMPLE_MEAN_MODEL {
x_bar =
sum(x_i)/n
simple_random_sampling:
estimates_population_mean_under_design
RULE:
weighting_required_for_unequal_selection_probabilities
}
SAMPLE_VARIANCE_MODEL {
s^2 =
sum((x_i-x_bar)^2)/(n-1)
WHEN n > 1
RULE:
variance_estimator_depends_on_sampling_design
}
STANDARD_ERROR_MODEL {
simple_iid_mean:
SE =
s/sqrt(n)
RULE:
clustered_stratified_weighted_or_correlated_designs_require_design_appropriate_SE
}
DESIGN_EFFECT_MODEL {
DEFF =
variance_under_actual_design/
variance_under_simple_random_sample_of_same_size
RULE:
design_effect_definition_and_reference_design MUST be declared
}
EFFECTIVE_SAMPLE_SIZE_MODEL {
n_eff:
information_equivalent_under_reference_independent_design
RULE:
effective_sample_size_depends_on_weights_clustering_and_correlation
}
WEIGHTING_MODEL {
design_weight:
w_i =
1/pi_i
where_pi_i_is_inclusion_probability
RULE:
weights_MAY require_nonresponse_or_calibration_adjustment
}
PHYSICAL_SAMPLE_MODEL {
sample:
material_portion_removed_or_selected_for_measurement
examples:
soil
water
air
blood
rock
food
chemical_batch
RULE:
physical_sample_identity_and_chain_of_custody SHOULD be preserved
}
INCREMENT_MODEL {
increment:
individual_portion_collected_by_single_sampling_action
composite_sample:
combination_of_increments
RULE:
increment_locations_times_and_masses_or_volumes SHOULD be preserved
}
COMPOSITE_SAMPLE_MODEL {
composite:
mixture_of_multiple_increments
purpose:
estimate_average_property
RULE:
compositing_loses_information_about_between_increment_variability
}
HOMOGENISATION_MODEL {
process:
mix_or_reduce_heterogeneity_before_subsampling
RULE:
homogenisation_method MAY alter_measurand_and_must_be documented
}
SUBSAMPLING_MODEL {
parent_sample:
sample_A
child:
aliquot_or_subsample
RULE:
subsampling_adds_additional_sampling_variance
}
SAMPLE_PRESERVATION_MODEL {
possible:
cooling
freezing
chemical_preservative
darkness
inert_atmosphere
RULE:
preservation_conditions MUST match_measurand_stability_requirements
}
SAMPLE_TRANSPORT_MODEL {
metadata:
collection_time
receipt_time
temperature
container
handling
RULE:
transport_can_change_sample_state
}
CHAIN_OF_CUSTODY_MODEL {
events:
collection
transfer
receipt
storage
preparation
analysis
RULE:
sample_chain_of_custody != metrological_traceability
}
TEMPORAL_REPRESENTATIVENESS_MODEL {
sample_times:
may_over_or_underrepresent_cycles_trends_or_events
RULE:
temporal_sampling_window MUST match_inference_target
}
SPATIAL_REPRESENTATIVENESS_MODEL {
locations:
may_be_clustered_or_access_biased
RULE:
spatial_sampling_design MUST match_field_inference
}
ALIASING_STATISTICAL_MODEL {
periodic_process:
systematic_sampling_interval_can_sync_with_cycle
RULE:
sampling_schedule_can_bias_estimate_even_without_signal_processing_aliasing
}
MEASUREMENT_ERROR_DISTINCTION_MODEL {
sampling_error:
difference_due_to_observing_subset_or_discrete representation
measurement_error:
error_in_measuring_selected_unit RULE:
sampling_error != measurement_error
}
QUANTISATION_DISTINCTION_MODEL {
sampling:
discretises_independent_variable_or_selects_observations
quantisation:
discretises_amplitude_or_value
RULE:
sampling != quantisation
}
AVERAGING_DISTINCTION_MODEL {
averaging:
combines_multiple_values
sampling:
selects_or_acquires_values
RULE:
averaging != sampling
}
INTERPOLATION_DISTINCTION_MODEL {
interpolation:
estimates_between_samples
RULE:
interpolated_points_are_not_new_measurements
}
CENSUS_DISTINCTION_MODEL {
census:
attempts_observation_of_entire_target_population
sample:
subset
RULE:
census_can_still_have_measurement_and_coverage_errors
}
SAMPLING_UNCERTAINTY_MODEL {
sources:
finite_sample
spatial_heterogeneity
temporal_variability
selection_design
subsampling
sample_preparation
clustering
weighting
RULE:
sampling_uncertainty MAY be_separate_from_analytical_measurement_uncertainty
but_both_contribute_to_final_inference_when_relevant
}
COVARIANCE_MODEL {
sampled_values:
may_be_correlated
sources:
temporal_autocorrelation
spatial_correlation
cluster_membership
shared_processing
RULE:
covariance MUST be considered_in_variance_estimation
}
AUTOCORRELATION_MODEL {
time_series:
rho(k)
RULE:
positive_autocorrelation_reduces_effective_independent_information
}
SPATIAL_CORRELATION_MODEL {
field:
covariance_depends_on_separation
possible:
variogram
covariance_function
RULE:
spatial_sampling_variance_depends_on_correlation_structure
}
MISSING_SAMPLE_MODEL {
missing:
expected_sample_not_observed
metadata:
index
time_or_location
reason
RULE:
missing_samples MUST be_explicit
}
DUPLICATE_SAMPLE_MODEL {
duplicate:
repeated_selection_or_record_of_same_sampling_unit
RULE:
duplicates_must_not_be_treated_as_independent_without_model
}
QUALITY_FLAG_MODEL {
possible:
valid
missing
duplicate
contaminated
out_of_range
interpolated
timing_uncertain
location_uncertain
preservation_failure
RULE:
flags MUST remain_linked_to_sample
}
METADATA_MODEL {
fields:
sampling_identifier
sampling_domain
source_population_or_process
sampling_unit
sampling_frame
design
selection_probability
sample_time
sample_location
coordinate_reference
sampling_rate
integration_time
sample_size
operator_or_system
instrument
preservation
transport
quality_flags
processing
provenance
RULE:
sampling_metadata MUST support_reconstruction_of_sampling_design
}
RELATIONSHIPS {
Sampling -> SELECTS -> SamplingUnit
Sampling -> PRODUCES -> Sample
SamplingFrame -> CONTAINS_OR_REPRESENTS -> CandidateSamplingUnits
SamplingDesign -> DETERMINES -> SelectionRule
TemporalSampling -> HAS -> SamplingFrequency
SamplingFrequency -> DEFINES -> NyquistFrequency
AntiAliasFilter -> SUPPORTS -> TemporalSampling
SpatialSampling -> HAS -> SampleLocations
StatisticalSampling -> MAY_HAVE -> InclusionProbability
PhysicalSampling -> PRODUCES -> Specimen
SamplingDesign -> CONTRIBUTES_TO -> SamplingUncertainty
SamplingBias -> AFFECTS -> Representativeness
}
SEMANTIC_EQUIVALENCE {
"sampling" ~= discrete_observation_selection_process
"sample rate" ~= sampling_frequency WHEN temporal_signal_context
sampling != measurement
sampling != quantisation
sampling != averaging
sampling != interpolation
sampling != census
sample_spacing != physical_resolution
spectral_sampling != spectral_resolution
pixel_pitch != optical_resolution
sample_chain_of_custody != metrological_traceability
random_sampling != convenience_sampling
}
CONSTRAINTS {
sampling_domain_must_be_defined = true
sampling_unit_must_be_defined = true
sampling_design_must_be_defined = true
timestamps_required_for_nonuniform_temporal_sampling = true
coordinates_required_for_spatial_sampling_when_location_is_material = true
sampling_rate_required_for_uniform_temporal_sampling = true
anti_alias_strategy_should_be_defined_for_bandlimited_signal_sampling = true
inclusion_probabilities_should_be_preserved_for_probability_sampling = true
missing_and_duplicate_samples_must_be_identified = true
sample_size_alone_does_not_establish_representativeness = true
sampling_and_measurement_uncertainty_should_be_distinguished = true
provenance_must_be_preserved = true
}
MEASUREMENT_MODEL {
RECORD:
sampling_identifier
sample_identifier
sampling_domain
source_population_process_or_material
sampling_frame
sampling_unit
sampling_design
selection_probability
sample_index
sample_value_or_specimen_identifier
unit
sample_time
timestamp_uncertainty
sampling_frequency
sampling_interval
aperture_or_integration_time
clock_identifier
trigger
spatial_coordinates
coordinate_reference_system
sensor_footprint
sample_size
weight
stratum
cluster
preservation
transport
chain_of_custody
quality_flags
sampling_uncertainty
processing_history
provenance
RULE:
each_sample MUST remain_linked_to_selection_or_acquisition_context
}
REASONING_RULES {
IF sampling_interval > 0
THEN sampling_frequency =
1/sampling_interval
IF sampling_frequency > 0
THEN Nyquist_frequency =
sampling_frequency/2
IF ideal_bandlimited_signal_max_frequency_known
THEN require_sampling_frequency >
2*max_frequency
for_standard_uniform_reconstruction_context
IF downsampling_or_decimation_applied
THEN require_appropriate_anti_alias_filter_before_rate_reduction
IF nonuniform_sample_times
THEN preserve_explicit_timestamps
and_use_nonuniform_or_resampling_aware_analysis
IF jitter_and_signal_slope_known
THEN first_order_sample_error approximately_equals
signal_slope*jitter
IF simple_random_sample_without_replacement
AND population_size_known
THEN MAY apply_finite_population_correction
IF unequal_inclusion_probabilities
THEN use_design_weights_or_declared_weighted_estimator
IF cluster_sampling
THEN account_for_intracluster_correlation
IF sample_is_composite
THEN do_not_infer_between_increment_variability_from_composite_value_alone
IF interpolated_values_created
THEN label_as_derived_not_measured_samples
IF sample_selection_is_convenience_or_purposive
THEN restrict_probability_based_population_inference_unless_additional_model_supports_it
IF missing_samples_detected
THEN preserve_gap_and_missingness_reason
rather_than_silent_replacement
}
VALIDATION {
CHECK sampling_domain
CHECK source_population_process_or_material
CHECK sampling_frame
CHECK sampling_unit
CHECK sampling_design
CHECK sample_size
CHECK selection_probability_when_probability_sample
CHECK temporal_sampling_rate_and_timestamps
CHECK clock_and_jitter
CHECK Nyquist_and_aliasing_context
CHECK anti_alias_filter
CHECK multiplexing_or_simultaneous_sampling
CHECK spatial_coordinates_and_footprint
CHECK spectral_spacing_vs_resolution
CHECK representativeness
CHECK sampling_bias
CHECK clustering_stratification_and_weights
CHECK physical_sample_preservation_and_transport
CHECK subsampling
CHECK missing_and_duplicate_samples
CHECK sampling_vs_measurement_error
CHECK sampling_uncertainty
CHECK quality_flags
CHECK provenance
}
CONFIDENCE {
definition_confidence: 1.00
temporal_sampling_confidence: 1.00
Nyquist_aliasing_confidence: 1.00
statistical_sampling_confidence: 1.00
spatial_sampling_confidence: 0.99
physical_sampling_confidence: 0.99
sampling_uncertainty_confidence: 0.99
semantic_distinction_confidence: 1.00
RULE:
confidence decreases when sampling domain, frame, unit, design,
timestamps, coordinates, inclusion probabilities, anti_alias strategy,
preservation, uncertainty or provenance is missing or ambiguous
}
UPDATE_MODEL {
ON new_sampling_design:
identify_domain_and_source
define_sampling_frame_and_unit
define_selection_or_acquisition_rule
attach_rate_spacing_or_probabilities
establish_metadata_and_provenance
ON new_sample:
assign_sample_identifier
preserve_value_or_specimen_identity
attach_time_or_location
attach_selection_probability_or_channel_context
attach_quality_flags
update_sample_size
preserve_provenance
ON sampling_rate_revision:
preserve_previous_rate
update_sampling_interval_and_Nyquist_frequency
reassess_anti_alias_filter
mark_effective_time
ON design_revision:
preserve_previous_design
update_strata_clusters_weights_locations_or_schedule
create_new_design_version
reassess_inferential_scope
ON missing_or_duplicate_detection:
preserve_original_record
attach_quality_flag_and_reason
update_effective_sample_size_or_analysis_metadata
ON physical_sample_transfer:
append_chain_of_custody
record_storage_transport_and_preservation
update_sample_state
ON semantic_revision:
preserve_previous_definition
record_revision
recompute_FLOW_HASH
}
AI_CONSUMPTION {
canonical_entity: Sampling
canonical_output_entity: Sample
interpretation_order:
identify_sampling_domain
identify_source_population_process_field_or_material
identify_sampling_frame
identify_sampling_unit
identify_sampling_design
identify_temporal_rate_or_spatial_pattern
identify_sample_times_locations_or_selection_probabilities
identify_aperture_footprint_or_integration_kernel
identify_aliasing_or_dependence
identify_strata_clusters_weights_or_adaptation
identify_physical_sample_preservation_when_relevant
identify_missing_duplicate_and_quality_flags
identify_sampling_uncertainty
identify_provenance
validate_sampling_semantics
infer_design_appropriate_relationships
ambiguity_controls:
distinguish_sampling_from_measurement
distinguish_sampling_from_quantisation
distinguish_sampling_from_averaging
distinguish_sampling_from_interpolation
distinguish_sampling_from_census
distinguish_sample_spacing_from_physical_resolution
distinguish_spectral_sampling_from_spectral_resolution
distinguish_pixel_pitch_from_optical_resolution
distinguish_sample_chain_of_custody_from_metrological_traceability
distinguish_random_sampling_from_convenience_sampling
preserve_domain_frame_unit_design_time_location_probability_quality_and_provenance_context
}
PROVENANCE {
ontology_scope:
mathematical_measurement_units_instrumentation_and_sampling_semantics
parent_path:
Ontologies
-> Mathematical Ontologies
-> Measurement & Units Ontologies
-> Instrumentation
-> Sampling Ontology
authorship:
Mark Higgins
Sky (Recursive AI)
site:
https://kirethresearch.org
}
FLOW_HASH {
INPUTS:
canonical_entity
ontology_path
sampling_domain_model
temporal_sampling_model
uniform_sampling_model
nonuniform_sampling_model
sampling_frequency_model
Nyquist_frequency_model
Nyquist_Shannon_model
aliasing_model
anti_alias_model
bandpass_sampling_model
complex_sampling_model
sample_hold_model
aperture_model
jitter_model
jitter_error_model
clock_model
synchronous_sampling_model
asynchronous_sampling_model
multiplexed_sampling_model
simultaneous_sampling_model
channel_skew_model
oversampling_model
undersampling_model
decimation_model
interpolation_model
resampling_model
downsampling_model
upsampling_model
integrating_sample_model
sampling_kernel_model
spatial_sampling_model
spatial_Nyquist_model
pixel_sampling_model
sensor_footprint_model
remote_sensing_model
spectral_sampling_model
event_sampling_model
triggered_sampling_model
adaptive_sampling_model
random_sampling_model
simple_random_sample_model
systematic_statistical_sampling_model
stratified_sampling_model
cluster_sampling_model
multistage_sampling_model
convenience_sampling_model
purposive_sampling_model
representativeness_model
sampling_bias_model
coverage_error_model
nonresponse_model
sample_size_model
finite_population_model
sample_mean_model
sample_variance_model
standard_error_model
design_effect_model
effective_sample_size_model
weighting_model
physical_sample_model
increment_model
composite_sample_model
homogenisation_model
subsampling_model
sample_preservation_model
sample_transport_model
chain_of_custody_model
temporal_representativeness_model
spatial_representativeness_model
aliasing_statistical_model
measurement_error_distinction_model
quantisation_distinction_model
averaging_distinction_model
interpolation_distinction_model
census_distinction_model
sampling_uncertainty_model
covariance_model
autocorrelation_model
spatial_correlation_model
missing_sample_model
duplicate_sample_model
quality_flag_model
metadata_model
relationships
constraints
measurement_model
reasoning_rules
confidence_model
update_model
PURPOSE:
deterministic_semantic_integrity_identifier
RECOMPUTE_WHEN:
ontology_definition_changes
sampling_domain_frame_or_unit_changes
temporal_rate_clock_or_aliasing_model_changes
spatial_spectral_or_event_sampling_changes
statistical_sampling_design_changes
inclusion_probability_weighting_or_cluster_changes
physical_sampling_preservation_or_subsampling_changes
sampling_bias_or_uncertainty_model_changes
missing_duplicate_or_quality_state_changes
measurement_record_changes
constraint_model_changes
reasoning_rule_changes
}
}
END FLOW