TriadicFrameworks
Overview

Datacenter Reports — Tensor Registry

RTT‑Inside • Operator‑First • Drift‑Bounded

The Datacenter Reports module exposes a set of structural, dimensional, and compute‑layer tensors used across RTT evaluators, dashboards, and cross‑module analysis. This document explains each tensor in plain language, including:

  • what the tensor represents
  • how it is shaped
  • how it is used
  • which RTT layers it participates in
  • how it propagates across modules
  • how to read it as a student or operator

All tensors listed here are defined in tensor_registry.py and validated against tensor_export.schema.json.

📦 What is a Tensor in RTT?#

A tensor is a structured, multi‑dimensional field representing a stable, operator‑meaningful slice of the world. In Datacenter Reports, tensors encode:

  • structural fields (facilities, governance, cultural substrate, standards, human envelope)
  • dimensional fields (planetary, cultural, governance, economic, compute, infrastructure)
  • compute‑layer fields (qCompute density, thermal envelope, energy envelope)

Each tensor is:

  • versioned
  • drift‑bounded
  • coherence‑scored
  • regime‑classified
  • AI‑parsable
  • cross‑module compatible

📚 Tensor Index#

Below are the canonical tensors exposed by this module.

1. structural_field_tensor#

Type: 2D tensor
Role: Structural field map
Shape: [n_layers, n_fields]
Analyzer Layer: triadic_stack
Dimensional Fields: compute, infrastructure, governance
Regime: stable
Coherence: ~0.92
Drift: ~0.03

What it represents#

A normalized structural snapshot of the datacenter across the five RTT structural fields:

  1. Facilities
  2. Governance
  3. Cultural Substrate
  4. Standards
  5. Human Envelope

Each row corresponds to a datacenter layer; each column corresponds to a structural field.

How to read it#

  • High values → strong structural alignment
  • Low values → weak or missing structural support
  • Row patterns → layer‑level strengths/weaknesses
  • Column patterns → field‑level strengths/weaknesses

Cross‑module propagation#

  • Governance Substrate
  • NoS
  • Integrations

2. dimensional_field_tensor#

Type: 2D tensor
Role: Dimensional field map
Shape: [n_dimensions, n_sites]
Analyzer Layer: planetary_layer
Dimensional Fields: planetary, cultural, economic
Regime: emergent
Coherence: ~0.88
Drift: ~0.05

What it represents#

A multi‑site comparison of dimensional fields across datacenter regions.

How to read it#

  • Rows = dimensions
  • Columns = sites
  • Values = normalized dimensional intensity

Cross‑module propagation#

  • Framework Field Theory
  • Low Dimensional Structures

3. qcompute_tensor#

Type: 2D tensor
Role: Compute capacity map
Shape: [n_sites, n_metrics]
Analyzer Layer: compute_infrastructure
Dimensional Fields: compute, infrastructure, planetary
Regime: transitional
Coherence: ~0.81
Drift: ~0.07

What it represents#

A normalized map of qCompute‑related metrics:

  • compute density
  • energy envelope
  • thermal regime

How to read it#

  • High values → strong compute capacity
  • Low values → constrained or thermally limited regions

Cross‑module propagation#

  • Inverted Economics
  • Resilience Checker

🧠 How Tensors Are Validated#

All tensors must conform to:

  • tensor_export.schema.json
  • validate_module_tensors.py
  • RTT drift + coherence rules
  • semantic versioning (vX.Y.Z)
  • lineage tracking

Validation ensures:

  • shape correctness
  • dtype correctness
  • provenance completeness
  • RTT metadata completeness
  • cross‑module safety

🔗 How Tensors Are Used in Plots#

The following plots consume these tensors:

  • heatmap_structural_fields → uses structural_field_tensor
  • interactive_dimensional_map → uses dimensional_field_tensor
  • qcompute_capacity_map → uses qcompute_tensor

See:
docs/datacenter_reports/plots/plot_registry.py

🧩 Cross‑Module Integration#

These tensors propagate into:

  • Governance Substrate
  • NoS
  • Integrations
  • Framework Field Theory
  • Low Dimensional Structures
  • Inverted Economics
  • Resilience Checker

This enables cross‑domain RTT analysis without drift.

🏁 Summary#

This registry defines the canonical tensor interface for the Datacenter Reports module. It is:

  • operator‑first
  • student‑ready
  • AI‑parsable
  • drift‑bounded
  • cross‑module compatible

Every tensor in this module is a stable, versioned, structural artifact of the RTT stack.

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