CCMM-GVP-001 | GABEY CONSULTING PTY LTD | APRIL 2026
CCMMGeospatial Validation Protocol
A structured validation programme demonstrating that CCMM convergence detection identifies high-impact, low-probability events before they occur — and resolves those events to a precise geographic coordinate using existing open-source data.
Domain Severe Weather (Proxy)
Data Sources BOM • ERA5 • NOAA
Case Region Queensland, Australia
Target ≥ 3.0 SD above null model
Phase 1Weather Proxy Domain
50+Validation Cases
3.0 SDPass Threshold
Open DataFully Verifiable
VALIDATED PERFORMANCE RECORD
CCMM has already been tested against real-world outcomes.
Before the GVP-001 weather validation programme began, CCMM produced prospective assessments against two high-consequence real-world events. Both are independently verifiable. Both are timestamped before the events resolved.
GEOPOLITICAL APPLICATION | MARCH 2026
2026 Iran War — Prospective CCMM Assessment
87.5%
Day 28 Convergence Score
8.5 SD
Above Null Model Mean
CCMM Day 28 Score87.5%
Null Model Baseline~11%
"Six weeks before the 2026 Iran War began, CCMM assigned a 0.0001% probability to the event. The war commenced 28 February 2026. The framework scored 87.5% against confirmed live events through Day 28 — 8.5 standard deviations above the random baseline."
Assessment timestamped: 7 March 2026 | SSRN Abstract ID 6364078 | Zenodo DOI 10.5281/zenodo.19382186
SATCOM CYBER APPLICATION | RETROSPECTIVE 2022
2022 KA-SAT Cyberattack — Five CCMM Threat Findings
5 / 5
Findings Confirmed Post-Hoc
75 Days
Before Formal Attribution
✓ Conditional timing signal — satcom interdiction linked to invasion
✓ Governance transition as probabilistic attack surface
✓ Civilian infrastructure spillover (67-79% probability assigned pre-event)
✓ Attribution ambiguity as deliberate threat vector
✓ Electronic warfare succession — confirmed by Viasat 2025
"All five findings were analytically derivable before the event. None were produced by MITRE ATT&CK for Space, STRIDE, or ISM. CCMM identified the conditions under which the attack became likely — including the governance gap, the spillover risk, and the successor EW threat."
Published: SSRN Abstract ID 6566478 | Zenodo DOI 10.5281/zenodo.19548175
CCMM-GVP-001 — PUBLIC DEMONSTRATION
Can you detect a disaster before it happens?
Traditional forecasting identifies a developing threat. CCMM goes further: it evaluates converging indicators, measures forecast confidence, and identifies the most probable impact zone before the event — then compares that forecast against the recorded outcome.
Public demonstration only. Core analytical mechanics are intentionally abstracted. This block explains the idea in plain language without disclosing the protected internal methodology.
Loading public-safe demonstration payload…
Forecast confidence Confidence is a convergence measure derived from multiple evidence classes. It is not a guarantee of outcome.
✓ CCMM FORECAST vs RECORDED OUTCOME
WARNING RAISEDRecorded pre-event warning point
FORECAST CONFIDENCEPublic-facing display metric
IMPACT-ZONE PRECISIONCompared against recorded outcome
RESULT GRADE
CCMM vs UNINFORMED BASELINE
This page presents a simplified public demonstration of the CCMM methodology for explanatory purposes only.
CCMM-GD-002 — CRITICAL INFRASTRUCTURE CASCADE
What fails next when one system goes down? That is where the real danger begins.
Most systems detect failure. CCMM models the path of consequence. It identifies how one disruption propagates through communications, coordination, energy and logistics — as the cascade begins to develop, before the downstream consequence fully forms.
How to think about this: The first failure is rarely the real problem. The real problem is the cascade — what breaks next, who loses visibility, which dependent systems start to degrade. This demonstration shows how CCMM turns one initial disruption into a map of downstream consequence, and compares that forecast against what was later observed.
CCMM Cascade Propagation PathWatch how a single disruption travels through dependent systems over time.
CCMM Cascade ConfidenceThis is a convergence measure of downstream consequence. It is not a guarantee of event outcome, but a confidence view of how dependency stress is forming and where it is heading.
Warning threshold reached — CCMM raises a cascade alert when convergence enters the actionable zone
⚠ Standard incident view saw…
✓ CCMM cascade model identified…
✓ CCMM CASCADE FORECAST vs OBSERVED CONSEQUENCE
INITIAL FAILUREFirst point of disruption
CASCADE CONFIDENCEPublic display metric
DEPENDENCY REACHAffected downstream layers
RESULT GRADE
CCMM CASCADE MODELLING vs STATIC INCIDENT VIEW
This page presents a simplified public demonstration of CCMM cascade modelling for explanatory purposes only. All scenarios are based on documented historical patterns. The CCMM scoring engine operates server-side; core methodology, weighting logic, and internal scenario architecture are not exposed in this interface.
Protection Notice
CCMM, its acronyms, methods, concepts, analytical structures, scoring approaches, terminology, scenario logic, model architecture, presentation logic, derivative formulations, explanatory frameworks and associated proprietary materials are protected works of GABEY Consulting Pty. Ltd., Australia.
Publications released by GABEY Consulting Pty Ltd under Creative Commons Attribution 4.0 International licence (CC BY 4.0) remain subject to the terms of that licence with respect to the specific published text. This notice governs the CCMM methodology, scoring architecture, proprietary implementation, and all materials not released under an open licence.
No part of the proprietary CCMM implementation, in whole or in part, may be extracted, copied, reproduced, adapted, reverse engineered, translated, republished, stored, incorporated, reformulated, transmitted, embedded, deployed or otherwise used in or for any software, algorithm, model, artificial intelligence system, machine learning workflow, corporate tool, operational process, consulting method, document, report, presentation, assessment framework, internal program, commercial service or derivative method without the prior express written permission of GABEY Consulting Pty. Ltd.
Unauthorised use, imitation, abstraction, derivation or repurposing of proprietary CCMM materials is strictly prohibited. All rights reserved.
