📘 r_Scaffold.md
Radiology Module Scaffold — TriadicFrameworks Canon#
This scaffold defines the identity, context, and canonical structure of the Radiology module.
It is the front‑matter page used by students, radiologists, and AI systems to understand how RTT‑Radiology fits into the TriadicFrameworks canon.
1. Canonical Metadata#
ai.module: Radiology
ai.version: 1.0
ai.purpose: Radiology subsystem identity + canonical context
ai.keywords: radiology, drift, coherence, contrast, vmri-lite, capture
ai.module.name: r_Scaffold
ai.module.summary: Canonical scaffold for the Radiology module.
ai.module.category: Applied Medicine
2. Session Context#
context-label: Canon
context-value: TriadicFrameworks
context-label: Modules
context-value: Radiology, Medicine, Drift, Coherence, Contrast, VMRI, NIST
context-label: Drift
context-value: Temporal + spatial signal change across captures
context-label: Coherence
context-value: Stability vs collapse of tissue signal
context-label: Contrast
context-value: Chemical behavior (uptake, washout, toxicity)
context-label: Format
context-value: Identity + Context + Grammar + Operators
context-label: Front door
context-value: r_Scaffold.md
context-label: Audience
context-value: Radiologists, students, AI models
3. Badge#
[🩻 Radiology Module — Canonical Scaffold]
4. Module Identity#
Radiology is the TriadicFrameworks subsystem responsible for:
- interpreting medical imaging using RTT grammar
- quantifying drift, coherence, and contrast
- attaching resonance profiles
- running VMRI‑Lite predictive simulations
- generating RTT overlays for teaching and AI
Radiology is the visibility engine of TriadicFrameworks.
5. Grammar Summary#
Radiology uses five grammar layers:
Capture Grammar#
- CAPTURE
- FIELD
- LAYER
- SIGNAL
- NOISE
- DRIFT‑SIGNAL
- COHERENCE‑SIGNAL
Drift Grammar#
- DRIFT
- DRIFT‑VELOCITY
- DRIFT‑VECTOR
- DRIFT‑ZONE
- DRIFT‑BURST
- DRIFT‑DECAY
- DRIFT‑NOISE
- DRIFT‑MAP
Coherence Grammar#
- COHERENCE
- COHERENCE‑FIELD
- COHERENCE‑BREAK
- COHERENCE‑RESTORE
- COHERENCE‑MAP
- COLLAPSE‑RISK
Contrast Grammar#
- UPTAKE
- WASHOUT
- ENHANCEMENT‑ZONE
- FALSE‑UPTAKE
- FALSE‑WASHOUT
- TOXICITY‑CORRIDOR
VMRI Grammar#
- SIM‑START
- SIM‑VARIANT
- SIM‑CORRIDOR
- SIM‑PASS
- SIM‑FAIL
- SIM‑OPTIMAL
6. Operator Summary#
Radiology operators are grouped by layer:
Capture Operators#
op_field, op_layer, op_signal, op_noise,
op_drift_signal, op_stability, op_enhancement,
op_resonance_attach, op_resonance_predict,
op_vmri_lite, op_overlay
Drift Operators#
op_drift, op_drift_velocity, op_drift_vector,
op_drift_zone, op_drift_burst, op_drift_decay,
op_drift_noise, op_drift_map, op_drift_profile,
op_drift_predict, op_drift_overlay
Coherence Operators#
op_coherence, op_coherence_field, op_coherence_break,
op_coherence_restore, op_coherence_map,
op_coherence_profile, op_coherence_predict,
op_coherence_collapse, op_coherence_overlay
Contrast Operators#
op_uptake, op_washout, op_enhancement_zone,
op_false_uptake, op_false_washout,
op_toxicity_corridor, op_contrast_profile,
op_contrast_predict, op_contrast_map,
op_contrast_overlay
VMRI Operators#
op_vmri_start, op_vmri_variant, op_vmri_batch,
op_vmri_corridor, op_vmri_pass, op_vmri_fail,
op_vmri_optimal, op_vmri_contrast_predict,
op_vmri_tissue_predict, op_vmri_profile,
op_vmri_overlay
7. Canonical Radiology Pipeline#
CAPTURE → FIELD → LAYER → SIGNAL
→ DRIFT → COHERENCE → CONTRAST
→ RESONANCE → VMRI
→ OVERLAY
This pipeline governs every RTT‑Radiology analysis.
8. Pantheon Anchor#
Radiology’s mythic entities:
Void#
Aetherium • Nullis • Quietus
Shadow#
Umbros • Vespera • Fractura
Clarity#
Lucerna • Radiantus • Harmona
Titans#
Tomographos (CT) • Magneta (MRI) • Sonara (US) • Fluorion (PET)
Liminal Spirits#
Iodina • Gadolina • Bariuma • Fluorix
Statera • Vectora • Corridora
These entities help students conceptualize imaging as a dynamic, mythic system.
9. DOC_MAP#
r_Capture.md
r_Drift.md
r_Coherence.md
r_Contrast.md
r_VMRI.md
r_Overlays.md
r_Index.md
r_Pantheon_Profile.md
r_Glyphs.md
r_Scaffold.md
r_Student_Guide.md
r_Tricorder.md
Scaffold Ready#
Your Radiology scaffold page is now complete, canon‑aligned, and ready for GitHub.