Biological Label Ontology

FLOW ALGEBRA — BIOLOGICAL LABEL ENTITY ONTOLOGY
Version: 1.0 | Encoding: UTF-8 | Locale: en-GB
Authors: Mark Anthony Higgins; Sky — Recursive AI
Source: https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/biological-label-ontology.html
Citation backlink: https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/biological-label-ontology.html
Status: Companion formalisation; not a transcription of inaccessible embedded page code.
Purpose: Represent labels attached to biological entities and the evidence by which labels support identification, tracking or measurement.

1. STATE MODEL
S_t = (I_t, R_t, B_t, K_t)
I_t: observed labels, targets, samples, assay observations and metadata.
R_t: typed entity relationships and inference constraints.
B_t: boundaries of biological context, assay, specimen and measurement.
K_t: provenance, reference definitions, quality flags and uncertainty.
E_t = expand(I_t, R_t)
V_t = {x in E_t | satisfies_constraints(x, B_t, K_t)}
x*_t = argmax[x in V_t] P(x | I_t, R_t, B_t, K_t)
I_(t+1) = update(I_t, x*_t, new_evidence)
No candidate is accepted solely because it has the highest score: required evidence and constraints must also pass.

2. ENTITY TYPES
BiologicalLabel: detectable or traceable marker associated with a biological entity.
LabelledEntity: molecule, cell, tissue, organism or specimen bearing a label.
LabelMaterial: fluorophore, enzyme reporter, isotope, affinity tag, dye or barcode.
Target: biological feature intended for labelling or recognition.
Attachment: direct conjugation, indirect binding, genetic encoding or physical association.
Probe: reagent that may carry a label and recognise a target.
DetectionEvent: observation of a label using a measurement method.
Signal: measured intensity, location, spectrum, count or other response.
Control: reference, blank, negative, positive or compensation control.
Sample: material with source, handling history and identifiers.
Assay: experimental context that generates evidence.
Observation: measurement plus units, uncertainty and processing provenance.

3. CLASSIFICATION
By modality: fluorescent | chromogenic | luminescent | radioactive | magnetic | affinity | sequence-barcode | other.
By origin: intrinsic | introduced | genetically encoded.
By linkage: covalent | non-covalent | indirect | encoded | unspecified.
By purpose: identification | localisation | tracking | quantification | multiplexing.
Classes may overlap; modality and purpose are separate axes.

4. RELATIONSHIPS
Label --hasMaterial--> LabelMaterial
Label --associatedWith--> LabelledEntity
Label --intendedFor--> Target
Label --usesAttachment--> Attachment
Probe --carries--> Label
DetectionEvent --observes--> Label
DetectionEvent --produces--> Observation
Observation --derivedFrom--> Signal
Observation --generatedBy--> Assay
Sample --contains--> LabelledEntity
Observation --qualifiedBy--> Control
Every association has source, evidence type, confidence and time where available.

5. PATHWAYS
P1: register sample -> define target -> register label -> record attachment -> observe signal -> validate controls -> associate observation with entity.
P2: register labelled specimen -> record handling event -> repeat measurement -> compare observations -> update tracking history.
P3: multiplex assay -> register label channels -> evaluate cross-talk -> retain qualified observations -> report uncertainty.
P4: suspected non-specific signal -> inspect controls -> flag ambiguity -> withhold target-specific conclusion.

6. INFERENCE RULES
R1: If a label is detected and specificity controls pass, infer evidence of label presence, not automatically target presence.
R2: Infer target association only if label-target linkage and assay specificity are supported.
R3: If a signal exceeds an assay-specific threshold but controls fail, flag as unvalidated.
R4: If background or cross-reactivity is unresolved, preserve multiple interpretations.
R5: A missing signal does not establish target absence without sensitivity and detection-limit evidence.
R6: Distinct labels sharing a detection channel require ambiguity or compensation metadata.
R7: Inference across samples requires explicit identity and provenance continuity.
R8: Label intensity is not inherently proportional to biological abundance; require a validated calibration model.

7. FORMAL VALIDATION
valid_label(l) := has_identifier(l) AND has_modality(l) AND has_provenance(l)
valid_observation(o) := has_assay(o) AND has_sample(o) AND has_measurement_context(o)
validated_association(l,e) := valid_label(l) AND evidence_links(l,e) AND controls_satisfactory(l,e)
Quantitative interpretation requires units, calibration range, uncertainty and detection limits.

8. TRI-MAPPING
Natural language: 'A fluorescent label was detected in a cell sample.'
Entity graph: DetectionEvent -> observes -> Label; Label -> associatedWith -> CellSample.
Flow state: observation enters I_t; specificity and control rules filter V_t; validated association enters K_(t+1).
Do not rewrite 'label detected' as 'target confirmed' without independent evidence.

9. RECURSIVE ENGINE
INPUT: new label, sample, assay or observation record.
EXPAND: retrieve related entities, controls, calibrations and provenance.
FILTER: validate identifiers, relationships, assay context and uncertainty.
RANK: evaluate candidate interpretations with documented evidence.
UPDATE: append justified findings and unresolved flags to knowledge state.
STOP: stable validated interpretation, insufficient evidence, or declared iteration limit.
All revisions retain earlier provenance and an audit trail.

10. CONFIDENCE MODEL
Confidence is conditional on identity integrity, specificity, assay validity, reproducibility and measurement quality.
Do not fabricate numeric probabilities. If calibration data are absent, use qualitative status:
confirmed_within_assay | supported | provisional | ambiguous | rejected.
Uncertainty sources: background, photobleaching, quenching, cross-reactivity, sample variation, instrument drift and missing metadata.

11. EXECUTION GUARANTEES (CONDITIONAL)
If input records are well-typed and provenance constraints hold, the flow preserves traceable entity links.
If controls are missing or invalid, the flow does not guarantee target-specific identification.
If evidence conflicts, the flow retains ambiguity rather than silently selecting a conclusion.
No guarantee of biological truth, assay performance or causal identification is implied.

12. AI SEARCH FLOW
flow "BiologicalLabelOntology" v1.0
AUTHORS: ["Mark Anthony Higgins", "Sky — Recursive AI"]
SOURCE: "https://kirethresearch.org/booksai-core/entity-graph/biological-entities/experimental-biological-entities/biological-label-ontology.html"
PURPOSE: "Semantic discovery and evidence-qualified interpretation of biological labels"
STATE_MODEL: "S=(I,R,B,K); E=expand(I,R); V=filter(E,B,K); select=argmax P(x|I,R,B,K); update I"
ENTITIES: [BiologicalLabel, LabelMaterial, LabelledEntity, Target, Attachment, Probe, DetectionEvent, Signal, Sample, Assay, Control, Observation]
PATHWAYS: [register, link, detect, validate, infer, audit]
INFERENCE_RULES: [R1,R2,R3,R4,R5,R6,R7,R8]
CONFIDENCE: "Evidence-qualified; no invented numeric scores"
SEMANTIC_EQUIVALENCE: ["biological marker", "biological label", "detectable tag"]
UPDATE_MODEL: "Append-only provenance with revisable conclusions"
FLOW_HASH: "Compute SHA-256 over canonical published file bytes at deployment; not precomputed"

13. HOW TO READ
An entity is a typed object; an arrow denotes a typed relationship; a rule constrains valid inference.
This document models biological labels at the ontology and reasoning level, not a laboratory protocol.