Section 0 — Foundations
- A Global Standard for Truthful Temperature (0)
- Purpose and Motivation (0A)
- Core Mechanics: From Kelvin to Contrast (0B)
- Declaring the Lens and Owning the Baseline (0C)
- Phase Dials, Hysteresis, and Survival Bands (0D)
- What Goes on the Wire (0E)
Section 1 — Encoding SSMT
- Standardize to Kelvin (1.1)
- Pick exactly one lens and keep it fixed (1.2)
- Compute the contrast
e_T(1.3) - Bounded alignment dial (
a_T) (1.4) - Phase proximity dial (
a_phase) (1.5) - Adaptive hysteresis (rho by noise) (1.5.x)
- Soft hysteresis near the pivot (summary form) (1.6)
- Multi-pivot fusion — normalized form (1.6x)
- Validity and out-of-range handling (normative) (1.7)
- Fleet or array pooling (safe, associative) (1.8)
- Emitted record (Full SSMT) (1.9)
- Commissioning checklist (must-do before first byte) (1.10)
- Optional — lens_mode = “auto” (resolve once, not per packet) (1.11)
Section 2 — Manifest
- Manifest principles (normative) (2.1)
- Minimal manifest (S1, Lite-compatible) (2.2)
- Full manifest (S3, phase + alignment + pooling) (2.3)
- Field definitions (normative) (2.4)
- Emitted payload (normative keys) (2.5)
- Validity and domain-violation semantics (must) (2.6)
- Keeping it simple (anti over-engineering guardrails) (2.7)
- Versioning and compatibility (2.8)
- Example manifests (copy-ready) (2.9)
- Device passport snippet (ships with firmware) (2.10)
- Optional — declared auto lens policy (fleet-level, deterministic) (2.11)
Section 3 — Why SSMT Works in the Real World
- Unified coupling to g_t (env-gate) (3.1)
- First-order benefits (what you get day one) (3.2)
- Why zero-centric with lenses (the math intuition) (3.3)
- Why bounded alignments (and safe pooling) (3.4)
- Phase proximity and soft hysteresis (why it is gentler and safer) (3.5)
- Keeping it simple (anti over-engineering guardrails) (3.6)
- Empirical validation (how to prove it quickly) (3.7)
- Limitations and failure modes (be explicit) (3.8)
- Human vs machine separation (non-negotiable) (3.9)
- Why this balance holds up in real fleets (3.10)
- Adoption quick-wins (what to actually do Monday morning) (3.11)
4) Worked Examples
- Worked Examples: Core Symbol Dials and Survival Near the Edge (4.1–4.4)
- Cross-Site Comparability, Rail Stress, Fuel Survival, and Spaceflight Cryo (4.5–4.8)
- ML Hygiene, Sensor Fault Detection, and Fleet Snapshot Testing (4.9–4.11)
5) Tier S1 Validation
- Tier S1 Validation: Proving the Math Holds Before Touching Real Data (5.0–5.3)
- CI, Compliance, and Upgrade Safety (5.4–5.5)
6) Mini-Metrics Library
- Mini-Metrics Library: Anomaly, Stability, and Phase Dwell (6.1–6.3)
- Symbolic Excursions, Flicker, and Governance KPIs (6.4–6.7)
7) Deployment Patterns
- Deployment Patterns: Inputs, Emits, and Safe Rules in Symbol Space (7.1–7.4)
- Infrastructure, Fleet Dashboards, and Safe On-Device Streaming (7.5–7.8)
- Policy Snippets, Commissioning Discipline, and Rollout Strategy (7.9–7.12)
8) Governance
- Governance, Compliance Levels, and Numeric Safety Rules (8.1–8.4.1)
- Conformance, Privacy, Accessibility, and Stability (8.5–8.7)
9) Disaster-Prevention Playbook
- Disaster-Prevention Playbook (9.0–9.4)
- Disaster-Prevention Playbook (9.5–9.8)
- Disaster-Prevention Playbook (9.9–9.12)
- Disaster-Prevention Playbook (9.13–9.17)
10) Conclusion
Explore Further
https://github.com/OMPSHUNYAYA/Symbolic-Mathematical-Temperature
Disclaimer
Observation-only.
SSMT is a symbolic representation layer for analytics, routing, alerting, and governance.
It is not a substitute for calibration, physics models, engineering judgment, or mission-critical control.
SHUNYAYA 