SSMT – Deployment Patterns: Inputs, Emits, and Safe Rules in Symbol Space (7.1–7.4)

How to actually roll SSMT out: what you read, what you emit, and what policy looks like.

Purpose.
Sections 7.1–7.4 give ready-to-use rollout blueprints for four high-value environments. For each environment:

Inputs = what you already measure, no new sensor fantasy.
Emit = the symbolic fields you should output (e_T, a_phase, etc.).
Rules = plain policy expressed in symbol space, not “°C here vs °F there.”
This is designed so teams can stand up Shunyaya Symbolic Mathematical Temperature (SSMT) in production without rewriting infrastructure.

7.1 Weather and climate operations

Inputs.
Raw temperature readings from stations or surfaces. Optional: a published T_ref(lat, lon, doy) so seasonal baseline and local conditions are captured honestly.

Emit.

e_T using the log lens by default.
Optionally a_phase to track freeze/near-freeze risk.

Rules (examples in symbol space).

Heat Advisory:
  e_T >= +0.8
  for >= 3 h

Cold Advisory:
  e_T <= -0.8
  for >= 3 h

Freeze Risk (surface):
  a_phase <= -0.10
  for >= 20 min

Key practice.

Pick one lens. Publish it. Freeze it.
The default is log because it handles wide swings and keeps e_T = 0 anchored at T_ref.
If you operate across both very small “comfort” drifts and very large extremes, consider hybrid and publish tau.
If operating near extreme cold, a qlog setup with alpha protects numeric safety near 0 K.
Whatever you choose, declare it once in the manifest and do not change it per reading.

Human vs machine.

Human dashboards can still render °C/°F.
Machine logic (alerts, routing, analytics, policy) must consume only e_T, a_phase, etc., not raw unit strings.

7.2 Cities and mobility (roads, rail, aviation)

Surfaces (roads, rails, wings) fail when they get too cold, too slick, too soft, or too stressed. You can model those risks as symbols.

Inputs.

Surface temperatures from pavement, rail, structural skin, or airframe surfaces.
Material pivots and tags in T_m_tag_list, for example: “water freeze point,” “diesel cloud point,” “alloy neutral bend point.”
These pivots define survival bands, not vanity numbers.

Emit.

e_T (symbolic contrast).
a_phase if you care about one critical pivot.
a_phase_fused plus Q_phase if you care about multiple pivots at once (for example, multiple materials).
Q_phase gives soft hysteresis so you don’t flap between OK / RISK / OK / RISK every few minutes.

Road example (symbolic rule).

Black Ice Risk:
  a_phase_road <= -0.15
  for >= 20 min

Clear Ice Risk:
  CLEAR_FREEZE
  when a_phase_road >= +0.10
  for >= 20 min

That means: don’t just scream “ice!” the instant you cross a pivot. Require depth and dwell.

Rail example (stress and deformation).

First, define neutral stress in linear form:

e_T_rail :=
  ( T_rail - T_neutral ) / DeltaT

Then assert a safety action:

Speed Restrict:
  e_T_rail >= +3.0
  for >= 10 min

Interpretation:

Instead of arguing “Was that 55 degrees or 52 degrees?” you’re talking in terms of how far you are from the published neutral band, normalized by DeltaT.
That’s auditable and portable.

Aviation-type example (de-ice / release).

De-ice Hold:
  a_phase_wing <= -0.05

Release:
  Q_phase >= 0.70

a_phase_wing measures which side of a safety pivot the surface is on (for example, “frozen vs clear”).
Q_phase is the soft memory dial — you only release when conditions have stayed safe long enough.

7.3 Supply chain, food safety, and cold chain

Here the core concern is “did it ever get too warm or too cold for too long,” and “can I prove it to someone else.”

Inputs.

Logger temperature readings from storage, transit, handling points.
Product-specific pivots T_m where relevant (for example, “do not let this freeze,” “do not let this exceed this band”).

Emit.

e_T (often linear lens in controlled bands because you care about tight deviations, not cosmic extremes).
Optionally a_phase and Q_phase to capture freeze risk and dwell.

Rules in symbol space.

Cooling burden / heat load over a period:

S-CDD :=
  sum_t max( e_T(t) - e* , 0 )

Reject if:
  S-CDD > 1.5

Freeze excursion:

Freeze excursion:
  a_phase_product <= -0.05
  for > 15 min

Why this is strong.

You don’t argue over “what exactly was the sensor reading in °C vs °F at minute 17.”
You ship the sequence { timestamp_utc, e_T, a_phase?, Q_phase?, manifest_id, health }.
Anyone with your manifest can replay the exact same call later.

Always include manifest_id.
That ID points to the declared lens, pivots, Kelvin floor, clamps, and guardrails. Without it, the numbers have no legal meaning. With it, you have a self-contained audit trail.

7.4 Healthcare and controlled storage

The goal here is not just to “stay in range,” but to prove that you stayed in range in a way that any reviewer can replay.

Inputs.

Device or room temperature readings from cabinets, rooms, transport cases, etc.
A chosen lens per appliance or zone.
Optional T_m values for “too cold to be safe,” “too hot to be safe,” or “comfort/survival band.”

Emit.

Always emit e_T.
Emit a_phase and Q_phase wherever freeze / overheating / human exposure matters.

Symbolic policies.

Cabinet stability:

Cabinet rule:
  e_T in [-1.0, +0.5]
  for most of the day

Alert if:
  e_T leaves that band
  for >= 10 min

Warm-chain quarantine:

Quarantine:
  e_T >= +0.8
  for >= 15 min

Read that carefully:

You’re not saying “above X °C throw it away.”
You’re saying “above this symbolic excursion from baseline, sustained for this long, trigger quarantine.”
That’s more portable, more transparent, and easier to replay later.

Why this matters.
Two different cabinets in two different environments can still be judged by the same symbolic rule, because e_T is normalized around each cabinet’s declared T_ref and lens. You’ve removed the “but our facility runs warmer so it’s fine” loophole.

Navigation
Previous: SSMT – Symbolic Excursions, Flicker, and Governance KPIs (6.4–6.7)
Next: SSMT – Infrastructure, Firmware, and Safe Streaming of Symbolic Telemetry (7.5–7.8)

Directory of Pages
SSMT – Table of Contents