Control Organism Ontology

FLOW ALGEBRA — CONTROL ORGANISM ENTITY ONTOLOGY
Version: 1.0 | Date: 2026-10-28 | Encoding: UTF-8
Authors: Mark Anthony Higgins — Sky, Recursive AI
Source: https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/control-organism-ontology.html
Status: Companion formalisation; not a verbatim extraction of embedded source code.

PURPOSE
Represent control organisms as experimental roles assigned to biological organisms within specific studies, conditions, and comparisons. Preserve experimental provenance, comparability, and uncertainty.

STATE MODEL
S_t = (I_t, R, B, K)
I_t = current assertions about organisms, cohorts, treatments, outcomes and observations
R = typed relationships and inference rules
B = validity, ethics, scope and evidence boundaries
K = provenance, ontology identifiers and experimental context
E_t = expand(I_t, R)
V_t = {x in E_t | satisfies(x, B)}
x*_t = argmax_{x in V_t} P(x | I_t, R, B, K)
I_(t+1) = update(I_t, x*_t, observations, provenance)

ENTITY CLASSES
- BiologicalOrganism: individual biological subject, not necessarily a model organism.
- ExperimentalOrganism: organism enrolled in an experiment.
- ControlOrganism: experimental organism assigned a control role in a defined comparison.
- TreatmentOrganism: experimental organism assigned an intervention role.
- ExperimentalCohort: collection of enrolled organisms with defined inclusion criteria.
- ControlGroup: cohort or arm serving as comparator.
- ExperimentalProtocol: documented study design and measurement plan.
- Intervention: exposure, treatment or manipulation being evaluated.
- BaselineCondition: condition used to establish a reference measurement.
- OutcomeMeasurement: observed result with unit, time and method.
- Confounder: variable that may affect treatment and outcome relationships.
- RandomisationRecord: evidence of allocation method, if used.
- BlindingRecord: evidence of masking, if used.
- ProvenanceRecord: origin, consent/approval where applicable, timestamps and chain of custody.

CONTROL ROLE CLASSIFICATION
- NegativeControl: expected not to exhibit the target intervention-specific effect.
- PositiveControl: expected to show a known detectable effect or assay response.
- VehicleControl: receives carrier without active intervention.
- ShamControl: receives a simulated procedure without the active component.
- UntreatedControl: receives no specified intervention.
- BaselineControl: provides reference state or pre-intervention measurement.
- MatchedControl: selected to match specified relevant characteristics.
- HistoricalControl: comparator drawn from earlier data; not automatically exchangeable.
NOTE: These are study-specific roles; an organism can have different roles across studies.

TYPED RELATIONSHIPS
ControlOrganism is_a ExperimentalOrganism
ControlOrganism participates_in ExperimentalProtocol
ControlOrganism member_of ControlGroup
ControlGroup comparator_for TreatmentGroup
ExperimentalOrganism has_taxon TaxonomicEntity
ExperimentalOrganism has_provenance ProvenanceRecord
ExperimentalOrganism observed_under ExperimentalCondition
ExperimentalOrganism has_measurement OutcomeMeasurement
ControlGroup has_control_type ControlRole
ExperimentalProtocol specifies OutcomeDefinition
ExperimentalProtocol records AllocationMethod
OutcomeMeasurement measured_at Timepoint
ControlGroup may_have Confounder

INVARIANTS AND BOUNDARIES
B1. A control designation requires a named study and comparator context.
B2. Taxonomic identity does not imply experimental control status.
B3. Controls and treatment arms must use compatible outcome definitions for comparison.
B4. Comparability is assessed, never assumed from a shared species label.
B5. Missing allocation, blinding, provenance or confounder information remains unknown.
B6. Association or difference alone does not establish causality.
B7. All observations retain measurement units, uncertainty and timestamps where available.
B8. Ethical approvals and welfare requirements are recorded where applicable; never inferred.

INFERENCE RULES
R1: organism_in_study(o,s) AND assigned_role(o,s,'control')
    => ControlOrganism(o,s), confidence=source_supported.
R2: control_type(o,s,'vehicle') AND vehicle_matches_treatment(s)
    => candidate_vehicle_comparator(o,s); not proof of adequate matching.
R3: shared_outcome_definition(c,t) AND compatible_measurement(c,t)
    => comparison_permitted(c,t), subject_to_confounder_review.
R4: missing_provenance(o) => flag(o,'provenance_incomplete').
R5: unmatched_confounder(c,t) => flag(comparison(c,t),'comparability_risk').
R6: historical_control(c) => flag(c,'temporal_and_selection_bias_review').
R7: observed_difference(c,t) => hypothesis_of_effect; NOT causal_conclusion.

RECURSIVE PATHWAYS
P1: organism -> study -> assigned arm -> control role -> outcome -> comparison.
P2: organism -> provenance -> eligibility -> condition -> observation -> confidence update.
P3: control arm -> matching criteria -> confounder assessment -> comparability verdict.
P4: study -> measurement -> anomaly -> evidence review -> revised inference.

TRI-MAPPING
Entity: ControlOrganism
  Ontology: experimental role of a BiologicalOrganism within a Study
  Algebra: role(o,s) = control, with B1-B8 enforced
  Search: 'control organism', 'experimental control', 'comparator organism'
Entity: ControlGroup
  Ontology: comparator arm in an experimental design
  Algebra: comparator_for(C,T,s)
  Search: 'control group', 'negative control', 'vehicle control'
Entity: OutcomeMeasurement
  Ontology: observed outcome with method, time and uncertainty
  Algebra: y(o,t,m) with unit and provenance
  Search: 'control outcome', 'baseline measurement'

CONFIDENCE MODEL
Evidence strength is contextual, not a fixed numerical certainty.
confidence = assess(source_reliability, completeness, comparability,
                    measurement_quality, replication, confounding)
Possible labels: supported | provisional | uncertain | contradicted.
Missing evidence must never be converted to affirmative evidence.

EXECUTION GUARANTEE (CONDITIONAL)
Given valid typed inputs, deterministic rule evaluation, preserved provenance,
and explicit boundary checks, the flow guarantees traceable rule application;
it does NOT guarantee biological truth, experimental validity or causal identification.

AI SEARCH FLOW
AUTHORS: Mark Anthony Higgins; Sky — Recursive AI
SOURCE: https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/control-organism-ontology.html
CITATION_BACKLINK: same as SOURCE
PURPOSE: resolve and compare control organism roles within experimental contexts
QUERY_ENTITIES: organism, study, control role, comparator, outcome, confounder
PATHWAYS: P1, P2, P3, P4
SEMANTIC_EQUIVALENCE: control organism ~= comparator organism only when study context agrees
RETRIEVAL_RULE: prefer study-specific, provenance-backed assertions
INFERENCE_POLICY: apply R1-R7 and boundaries B1-B8
UPDATE_MODEL: append timestamped evidence; retain superseded claims for audit
FLOW_HASH: compute SHA-256 over canonical published file at deployment
LICENCE: refer to current Kireth Research publication licence; no licence terms inferred here

END FLOW