`GRAVITY-`

Forensic Research Aggregation & Epistemic Intelligence System

A seven-phase forensic research operating system that collects, correlates, scores, and tracks
publicly available information with cryptographic integrity and blockchain anchoring guarantees.

_{The system does not confirm or deny claims. It gathers, organizes, correlates, scores, and tracks data.}

Case File

Project Anchor — August 12 Gravity Event — Thomas Webb

#	Section	Category	Description
1	System Architecture	🟦	High-level design, data flow, phase pipeline
2	Phase Pipeline Overview	🟦	Phase dependency graph and capability matrix
3	Repository Structure	🟦	Complete file tree (36 modules, 6 test suites)
4	Phase I — Research & Collection	🟩	Data scraping, PDF analysis, physics, NLP, IPFS
5	Phase II — Cryptographic Integrity	🟩	Ed25519, Merkle trees, FOIA, audit reports
6	Phase III — Mathematical Framework	🟩	Equation parsing, dimensional analysis, claim graph
7	Phase IV — Quantitative Scoring	🟩	Bayesian confidence, entropy, citation density
8	Phase V — Temporal Dynamics	🟩	Timelines, drift kinematics, stability, alerts
9	Phase VI — Source Intelligence	🟩	Reputation, influence, coordination, provenance
10	Phase VII — Scientific Optimization	🟩	Math analysis, Rust contracts, blockchain anchoring
11	CLI Command Reference	🟧	All 87 commands organized by phase
12	Database Schema	🟪	39 tables across 7 phases
12	Scoring & Algorithm Reference	🟥	Mathematical formulas, weights, thresholds
13	Flow Diagrams	🟥	Data pipeline, scoring cascade, alert flow
15	Testing	⬜	390 tests, per-phase breakdown
15	IPFS Integration	⬜	Proof chain, pinning, IPNS workflow
16	Operational Scope & Reproducibility	⬜	Legal boundaries, audit trail, portability
17	Quick Start	⬜	Installation and first run
18	Dependencies	⬜	Required packages and versions

🟦 System Architecture

graph TB
    subgraph INPUT["📥 DATA SOURCES"]
        direction LR
        R[Reddit / Social]
        W[Wayback Machine]
        G[Gov Records]
        A[Academic DBs]
        P[PDF Documents]
        F[FOIA Documents]
    end

    subgraph PHASE1["🟢 PHASE I — Collection"]
        direction LR
        SC[Scrapers]
        PA[PDF Analyzer]
        PE[Physics Engine]
        NLP[NLP Analyzer]
    end

    subgraph PHASE2["🔵 PHASE II — Integrity"]
        direction LR
        CR[Ed25519 Crypto]
        MK[Merkle Trees]
        FO[FOIA Forensics]
        AU[Audit Reports]
    end

    subgraph PHASE3["🟡 PHASE III — Math"]
        direction LR
        EQ[Equation Parser]
        DA[Dim. Analysis]
        CG[Claim Graph]
        SY[SymPy CAS]
    end

    subgraph PHASE4["🟠 PHASE IV — Scoring"]
        direction LR
        BC[Bayesian Scorer]
        ME[Mutation Entropy]
        CD[Citation Density]
        CA[Contradictions]
    end

    subgraph PHASE5["🔴 PHASE V — Temporal"]
        direction LR
        CT[Conf. Timeline]
        ET[Entropy Trend]
        DK[Drift Kinem.]
        AL[Alert Engine]
    end

    subgraph PHASE6["🟣 PHASE VI — Intelligence"]
        direction LR
        SR[Source Reputation]
        IN[Influence Network]
        CO[Coordination Det.]
        DP[Deep Provenance]
    end

    subgraph PHASE7["⚪ PHASE VII — Scientific Optimization"]
        direction LR
        MF[Missing Factors]
        SA[Stability Analysis]
        FP[Formal Proofs]
        BA[Blockchain Anchors]
    end

    subgraph STORAGE["💾 STORAGE LAYER"]
        DB[(SQLite<br/>39 Tables)]
        IPFS[(IPFS<br/>Proof Chain)]
        LOG[Logs]
        CHAIN[(Blockchain<br/>Anchors)]
    end

    subgraph OUTPUT["📤 OUTPUT"]
        RPT[Reports]
        CLI[CLI / 87 Commands]
        DASH[Dashboard]
    end

    INPUT --> PHASE1
    PHASE1 --> PHASE2
    PHASE2 --> PHASE3
    PHASE3 --> PHASE4
    PHASE4 --> PHASE5
    PHASE5 --> PHASE6
    PHASE6 --> PHASE7

    PHASE1 --> STORAGE
    PHASE2 --> STORAGE
    PHASE3 --> STORAGE
    PHASE4 --> STORAGE
    PHASE5 --> STORAGE
    PHASE6 --> STORAGE
    PHASE7 --> STORAGE

    STORAGE --> OUTPUT

    style INPUT fill:#1a1a2e,stroke:#58a6ff,color:#c9d1d9
    style PHASE1 fill:#0d2818,stroke:#2ea043,color:#c9d1d9
    style PHASE2 fill:#0d1b2e,stroke:#58a6ff,color:#c9d1d9
    style PHASE3 fill:#2e2a0d,stroke:#d29922,color:#c9d1d9
    style PHASE4 fill:#2e1a0d,stroke:#f0883e,color:#c9d1d9
    style PHASE5 fill:#2e0d0d,stroke:#f85149,color:#c9d1d9
    style PHASE6 fill:#1f0d2e,stroke:#a371f7,color:#c9d1d9
    style PHASE7 fill:#1a1a1a,stroke:#8b949e,color:#c9d1d9
    style STORAGE fill:#161b22,stroke:#8b949e,color:#c9d1d9
    style OUTPUT fill:#0d1117,stroke:#58a6ff,color:#c9d1d9

🟦 Phase Pipeline Overview

Capability Matrix

%%{init: {'theme': 'dark', 'themeVariables': {'fontSize': '14px'}}}%%
graph LR
    subgraph LEGEND["PHASE LEGEND"]
        direction TB
        L1["🟢 I: Collection — 8 modules — 9 tests"]
        L2["🔵 II: Integrity — 6 modules — 24 tests"]
        L3["🟡 III: Math — 5 modules — 34 tests"]
        L4["🟠 IV: Scoring — 6 modules — 42 tests"]
        L5["🔴 V: Temporal — 6 modules — 75 tests"]
        L6["🟣 VI: Intelligence — 5 modules — 100 tests"]
    end

    L1 --> L2 --> L3 --> L4 --> L5 --> L6

    style L1 fill:#0d2818,stroke:#2ea043,color:#7ee787
    style L2 fill:#0d1b2e,stroke:#58a6ff,color:#79c0ff
    style L3 fill:#2e2a0d,stroke:#d29922,color:#e3b341
    style L4 fill:#2e1a0d,stroke:#f0883e,color:#ffa657
    style L5 fill:#2e0d0d,stroke:#f85149,color:#ff7b72
    style L6 fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style LEGEND fill:#0d1117,stroke:#30363d,color:#c9d1d9

Phase Statistics

Phase	Color	Name	Modules	Tests	Tables	CLI Commands
I	🟢	Research & Collection	8	9	9	21
II	🔵	Cryptographic Integrity	6	24	7	12
III	🟡	Mathematical Framework	5	34	6	11
IV	🟠	Quantitative Scoring	6	42	3	8
V	🔴	Temporal Dynamics	6	75	4	11
VI	🟣	Source Intelligence	5	100	4	11
		TOTALS	36	284	33	77

Phase Dependency Flow

%%{init: {'theme': 'dark'}}%%
flowchart LR
    I["🟢 Phase I\nCollection\n8 modules"]
    II["🔵 Phase II\nIntegrity\n6 modules"]
    III["🟡 Phase III\nMath\n5 modules"]
    IV["🟠 Phase IV\nScoring\n6 modules"]
    V["🔴 Phase V\nTemporal\n6 modules"]
    VI["🟣 Phase VI\nIntelligence\n5 modules"]

    I -->|"raw data"| II
    II -->|"verified data"| III
    III -->|"structured claims"| IV
    IV -->|"scored claims"| V
    V -->|"temporal profiles"| VI

    I -.->|"direct feed"| III
    I -.->|"direct feed"| IV
    III -.->|"graph data"| VI

    style I fill:#0d2818,stroke:#2ea043,color:#7ee787,stroke-width:2px
    style II fill:#0d1b2e,stroke:#58a6ff,color:#79c0ff,stroke-width:2px
    style III fill:#2e2a0d,stroke:#d29922,color:#e3b341,stroke-width:2px
    style IV fill:#2e1a0d,stroke:#f0883e,color:#ffa657,stroke-width:2px
    style V fill:#2e0d0d,stroke:#f85149,color:#ff7b72,stroke-width:2px
    style VI fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff,stroke-width:2px

🟦 Repository Structure

project-anchor-research/
├── main.py                              # CLI orchestrator (77 commands)
├── requirements.txt                     # Python dependencies
├── README.md
├── data/                                # SQLite DB, keys, downloaded files
│   └── keys/                            # Ed25519 signing keypairs
├── logs/                                # Timestamped operation logs
├── reports/                             # Generated reports
│   └── audits/                          # Audit reports (JSON, HTML, Markdown)
│
├── src/
│   ├── config.py                        # Configuration & constants
│   ├── database.py                      # SQLite schema (33 tables) & helpers
│   ├── logger.py                        # Structured logging
│   │
│   ├── collectors/                      # 🟢 Phase I — Data collection
│   │   ├── base_scraper.py              #   Abstract base with rate limiting
│   │   ├── reddit_scraper.py            #   Reddit JSON endpoint scraper
│   │   ├── wayback_scraper.py           #   Internet Archive CDX API
│   │   └── web_search_scraper.py        #   DuckDuckGo HTML scraper
│   │
│   ├── analyzers/                       # 🟢 Phase I — Document analysis
│   │   └── pdf_analyzer.py              #   PDF metadata, fonts, markings
│   │
│   ├── crossref/                        # 🟢 Phase I — External databases
│   │   ├── academic_records.py          #   CrossRef, Semantic Scholar, OpenAlex
│   │   ├── government_records.py        #   NASA NTRS, FOIA.gov, FPDS
│   │   └── research_sources.py          #   Extended source search
│   │
│   ├── physics/                         # 🟢 Phase I — Physics verification
│   │   ├── gravity_engine.py            #   Gravitational physics computations
│   │   └── wave_engine.py               #   Wave science computations
│   │
│   ├── nlp/                             # 🟢 Phase I — Narrative analysis
│   │   └── narrative_analyzer.py        #   Pattern detection & similarity
│   │
│   ├── ipfs/                            # 🟢 Phase I — Immutable storage
│   │   ├── ipfs_client.py               #   Kubo RPC API client
│   │   ├── proof_chain.py               #   DAG-linked evidence chain
│   │   ├── evidence_archiver.py         #   Orchestrates pinning to IPFS
│   │   ├── ipns_publisher.py            #   IPNS name publishing
│   │   └── multi_gateway.py             #   Multi-gateway health & pinning
│   │
│   ├── dashboard/                       # 🟢 Phase I — Visualization
│   │   └── dashboard.py                 #   Plotly/Dash interactive dashboard
│   │
│   ├── crypto/                          # 🔵 Phase II — Cryptographic integrity
│   │   └── signature_manager.py         #   Ed25519 keypair & CID signing
│   │
│   ├── proofs/                          # 🔵 Phase II — Merkle verification
│   │   └── merkle_snapshot.py           #   Merkle tree snapshots of DB state
│   │
│   ├── foia/                            # 🔵 Phase II — FOIA forensics
│   │   ├── foia_ingester.py             #   FOIA document ingestion
│   │   └── document_forensics.py        #   Document authenticity scoring
│   │
│   ├── investigations/                  # 🔵 Phase II — Case databases
│   │   ├── scientist_cases.py           #   Historical scientist cases DB
│   │   └── tesla_module.py              #   Tesla investigation module
│   │
│   ├── reports/                         # 🔵 Phase II — Audit reports
│   │   └── audit_generator.py           #   Comprehensive audit report gen
│   │
│   ├── taxonomy/                        # 🔵 Phase II — Knowledge base
│   │   └── knowledge_base.py            #   Taxonomy classification system
│   │
│   ├── math/                            # 🟡 Phase III — Mathematical framework
│   │   ├── equation_parser.py           #   Plaintext & LaTeX → SymPy AST
│   │   ├── dimensional_analyzer.py      #   Dimensional consistency checking
│   │   ├── symbolic_refactor.py         #   CAS: simplify, factor, diff
│   │   ├── derivation_logger.py         #   Step-by-step derivation chains
│   │   └── equation_audit_report.py     #   Math forensics audit reports
│   │
│   └── graph/                           # 🟡🟠🔴🟣 Phases III–VI
│       ├── claim_graph.py               #   🟡 III: Typed claim/source/entity graph
│       ├── propagation_graph.py         #   🟡 III: NetworkX propagation mapping
│       ├── confidence_scorer.py         #   🟠 IV: Bayesian 6-component scoring
│       ├── mutation_entropy.py          #   🟠 IV: Shannon entropy of mutations
│       ├── citation_density.py          #   🟠 IV: Cross-reference density scoring
│       ├── contradiction_analyzer.py    #   🟠 IV: Tension mapping & conflict clusters
│       ├── propagation_tracker.py       #   🟠 IV: Event velocity & amplification
│       ├── claim_scoring_report.py      #   🟠 IV: Aggregate epistemic reports
│       ├── confidence_timeline.py       #   🔴 V: Temporal confidence tracking
│       ├── entropy_trend.py             #   🔴 V: H(t) series, dH/dt, d²H/dt²
│       ├── drift_kinematics.py          #   🔴 V: Velocity, acceleration, jerk
│       ├── stability_classifier.py      #   🔴 V: 5-state epistemic classifier
│       ├── alert_engine.py              #   🔴 V: Rule-based anomaly detection
│       ├── lifecycle_report.py          #   🔴 V: 10-section lifecycle reports
│       ├── source_reputation.py         #   🟣 VI: EMA credibility tracking
│       ├── influence_network.py         #   🟣 VI: Source amplification graphs
│       ├── coordination_detector.py     #   🟣 VI: Temporal clustering detection
│       ├── provenance_deep.py           #   🟣 VI: Multi-layer origin tracing
│       └── source_forensics_report.py   #   🟣 VI: Comprehensive intelligence reports
│
└── tests/
    ├── test_physics.py                  # 🟢   9 tests — Physics engine
    ├── test_phase2.py                   # 🔵  24 tests — Crypto & integrity
    ├── test_phase3.py                   # 🟡  34 tests — Math & claim graph
    ├── test_phase4.py                   # 🟠  42 tests — Scoring engine
    ├── test_phase5.py                   # 🔴  75 tests — Temporal dynamics
    └── test_phase6.py                   # 🟣 100 tests — Source intelligence

🟩 Phase I — Research & Collection

🟢 Core data gathering and analysis layer

%%{init: {'theme': 'dark'}}%%
flowchart LR
    subgraph SOURCES["External Sources"]
        S1["🌐 Reddit"]
        S2["📚 Wayback"]
        S3["🏛️ Gov DBs"]
        S4["🎓 Academic"]
        S5["📄 PDFs"]
    end

    subgraph ENGINES["Processing Engines"]
        E1["Scraper Engine"]
        E2["PDF Analyzer"]
        E3["Physics Engine"]
        E4["NLP Engine"]
    end

    subgraph OUT["Outputs"]
        O1["📊 Reports"]
        O2["📌 IPFS Archive"]
        O3["💾 Database"]
    end

    S1 & S2 --> E1
    S3 & S4 --> E1
    S5 --> E2
    E1 --> E3
    E1 --> E4
    E2 --> O3
    E3 --> O1
    E4 --> O3
    E1 --> O2

    style SOURCES fill:#0d2818,stroke:#2ea043,color:#7ee787
    style ENGINES fill:#0d2818,stroke:#2ea043,color:#7ee787
    style OUT fill:#161b22,stroke:#8b949e,color:#c9d1d9

#	Module	Description
1	Data Collection	Scrapes Reddit, Wayback Machine, web search for earliest references
2	Document Analysis	PDF metadata extraction, font analysis, classification marking detection
3	Origin Trace	Identifies earliest indexed references, maps repost sequences
4	Government Cross-Ref	Searches NASA NTRS, FOIA.gov, FPDS, USASpending
5	Academic Verification	Searches CrossRef, Semantic Scholar, OpenAlex
6	Physics Consistency	GW strain, binding energy, tidal forces, merger energetics
7	Narrative Analysis	Detects whistleblower/disappearance/urgency patterns via NLP
8	IPFS Evidence Archive	Immutable, content-addressed proof chain on IPFS

🟩 Phase II — Cryptographic Integrity

🔵 Tamper-proof evidence anchoring and expanded research capabilities

%%{init: {'theme': 'dark'}}%%
flowchart LR
    subgraph CRYPTO["Cryptographic Layer"]
        K["🔑 Ed25519\nKeypair Gen"]
        S["✍️ CID Signing"]
        V["✅ Verification"]
    end

    subgraph MERKLE["Merkle Layer"]
        M["🌳 DB Snapshot"]
        MV["🔍 Integrity\nVerification"]
    end

    subgraph RESEARCH["Extended Research"]
        FO["📋 FOIA Forensics"]
        SC["🔬 Scientist Cases"]
        AU["📊 Audit Reports"]
    end

    K --> S --> V
    M --> MV
    FO --> AU
    SC --> AU

    style CRYPTO fill:#0d1b2e,stroke:#58a6ff,color:#79c0ff
    style MERKLE fill:#0d1b2e,stroke:#58a6ff,color:#79c0ff
    style RESEARCH fill:#0d1b2e,stroke:#58a6ff,color:#79c0ff

#	Module	Description
9	Ed25519 Signatures	Generate keypairs, sign CIDs, verify signatures
10	Merkle Snapshots	Hash entire DB state into Merkle tree, verify integrity
11	FOIA Forensics	Document authenticity scoring and classification detection
12	Scientist Cases DB	Historical cases of suppressed/disputed scientists
13	Audit Reports	Comprehensive HTML/JSON/Markdown audit generation
14	Taxonomy Knowledge Base	Classification system for organizing research categories

🟩 Phase III — Mathematical Framework

🟡 Symbolic computation and structured evidence graph

%%{init: {'theme': 'dark'}}%%
flowchart TB
    subgraph PARSE["Equation Processing"]
        P1["📝 Plaintext\nInput"]
        P2["📐 LaTeX\nInput"]
        P3["🔧 SymPy AST\nConversion"]
    end

    subgraph ANALYSIS["Mathematical Analysis"]
        A1["📏 Dimensional\nChecking"]
        A2["🧮 Symbolic\nRefactoring"]
        A3["📖 Derivation\nLogging"]
    end

    subgraph GRAPH["Evidence Graph"]
        G1["🔗 Claim Nodes"]
        G2["📚 Source Nodes"]
        G3["👤 Entity Nodes"]
        G4["⚡ Weighted Edges"]
    end

    P1 & P2 --> P3
    P3 --> A1 & A2 & A3
    A1 & A2 & A3 --> GRAPH

    style PARSE fill:#2e2a0d,stroke:#d29922,color:#e3b341
    style ANALYSIS fill:#2e2a0d,stroke:#d29922,color:#e3b341
    style GRAPH fill:#2e2a0d,stroke:#d29922,color:#e3b341

#	Module	Description
15	Equation Parser	Plaintext & LaTeX → SymPy AST with SHA-256 fingerprints
16	Dimensional Analyzer	Verify dimensional consistency of physics equations
17	Symbolic Refactor	CAS operations: simplify, factor, expand, differentiate, series
18	Derivation Logger	Step-by-step mathematical derivation chains with persistence
19	Claim Graph	Typed nodes (claims, sources, entities) with weighted edges

🟩 Phase IV — Quantitative Scoring

🟠 Bayesian scoring engine and quantitative analysis

%%{init: {'theme': 'dark'}}%%
flowchart LR
    subgraph INPUTS["Score Inputs"]
        I1["Prior Probability"]
        I2["Source Credibility"]
        I3["Citation Density"]
        I4["Contradiction Map"]
        I5["Verification Status"]
        I6["Mutation Decay"]
    end

    subgraph ENGINE["Bayesian Engine"]
        BE["⚖️ Weighted\nComposite\nScorer"]
    end

    subgraph OUTPUTS["Score Outputs"]
        O1["📊 Confidence\nScore 0–1"]
        O2["📋 Ranking\nReport"]
        O3["⚠️ Flags &\nAnomalies"]
    end

    I1 & I2 & I3 --> BE
    I4 & I5 & I6 --> BE
    BE --> O1 & O2 & O3

    style INPUTS fill:#2e1a0d,stroke:#f0883e,color:#ffa657
    style ENGINE fill:#2e1a0d,stroke:#f0883e,color:#ffa657
    style OUTPUTS fill:#2e1a0d,stroke:#f0883e,color:#ffa657

#	Module	Description
20	Confidence Scorer	6-component Bayesian scoring: prior, credibility, citation, contradiction, verification, mutation decay
21	Mutation Entropy	Shannon entropy of claim text mutations, drift velocity, semantic stability
22	Citation Density	Cross-reference density scoring with quality weighting
23	Contradiction Analyzer	Tension mapping, conflict cluster detection (union-find), contested claim identification
24	Propagation Tracker	Event logging, propagation velocity, cascade depth, amplification factor
25	Scoring Reports	Aggregate epistemic reports with integrity scores and rankings

🟩 Phase V — Temporal Dynamics

🔴 Temporal tracking, kinematic analysis, stability classification, and alerting

%%{init: {'theme': 'dark'}}%%
flowchart TB
    subgraph SIGNALS["Temporal Signals"]
        S1["dC/dt\nConfidence Rate"]
        S2["dH/dt\nEntropy Velocity"]
        S3["d²H/dt²\nEntropy Accel."]
        S4["d³d/dt³\nDrift Jerk"]
    end

    subgraph CLASSIFIER["State Machine"]
        C1["🟢 Stable"]
        C2["🔵 Converging"]
        C3["🟡 Volatile"]
        C4["🟠 Diverging"]
        C5["🔴 Critical"]
    end

    subgraph ALERTS["Alert Engine"]
        A1["ℹ️ Info"]
        A2["⚠️ Warning"]
        A3["🚨 Critical"]
    end

    S1 & S2 & S3 & S4 --> CLASSIFIER
    CLASSIFIER --> ALERTS
    C1 -.-> C2 -.-> C3 -.-> C4 -.-> C5

    style SIGNALS fill:#2e0d0d,stroke:#f85149,color:#ff7b72
    style CLASSIFIER fill:#2e0d0d,stroke:#f85149,color:#ff7b72
    style ALERTS fill:#2e0d0d,stroke:#f85149,color:#ff7b72

#	Module	Description
26	Confidence Timeline	Temporal confidence tracking with SMA/EMA, plateau detection, convergence analysis, dC/dt
27	Entropy Trend	H(t) time series, first derivative dH/dt, second derivative d²H/dt², spike/collapse detection
28	Drift Kinematics	Velocity dd/dt, acceleration d²d/dt², jerk d³d/dt³, inflection point detection, kinematic phase classification
29	Stability Classifier	5-state epistemic state machine: stable → converging → volatile → diverging → critical
30	Alert Engine	Rule-based anomaly detection across 9 alert types (entropy spike, confidence collapse, drift acceleration, tension surge, etc.)
31	Lifecycle Report	10-section narrative report with trajectory scoring (0–100%), grade scale (A–F), actionable recommendations

🟩 Phase VI — Source Intelligence & Network Forensics

🟣 Source-level credibility tracking, influence network analysis, coordination detection, and deep provenance tracing

%%{init: {'theme': 'dark'}}%%
flowchart TB
    subgraph REPUTATION["Source Reputation"]
        R1["📊 EMA Credibility\nα = 0.3"]
        R2["📈 Reliability Index\n4-component"]
        R3["🏷️ A–F Grading"]
    end

    subgraph NETWORK["Influence Network"]
        N1["🔗 Edge\nConstruction"]
        N2["📊 Centrality\nAnalysis"]
        N3["🎯 Gateway\nDetection"]
    end

    subgraph COORD["Coordination Detection"]
        D1["⏱️ Temporal\nClustering"]
        D2["🎭 Pattern\nClassification"]
        D3["📊 Scoring\n0–1"]
    end

    subgraph PROV["Deep Provenance"]
        P1["🔍 Mutation\nChain Walk"]
        P2["🏷️ Origin\nClassification"]
        P3["📉 Confidence\nDecay 0.85×"]
    end

    subgraph REPORT["Forensics Report"]
        F1["📋 Single Source\n5 sections"]
        F2["🌐 Ecosystem\n7 sections"]
        F3["💊 Health\nAssessment"]
    end

    REPUTATION --> REPORT
    NETWORK --> REPORT
    COORD --> REPORT
    PROV --> REPORT

    style REPUTATION fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style NETWORK fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style COORD fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style PROV fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style REPORT fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff

#	Module	Description
32	Source Reputation	EMA credibility tracking, Laplace-smoothed reliability, 4-component reliability index, A–F grading, trend direction
33	Influence Network	Source-to-source amplification edges, NetworkX centrality analysis, gateway/bottleneck detection, PageRank
34	Coordination Detector	Temporal burst/cascade/simultaneous pattern detection, sliding window clustering, coordination scoring
35	Deep Provenance	Mutation chain + source chain traversal, origin classification (original/derived/mutated/amplified/orphan), confidence decay
36	Source Forensics Report	7-section ecosystem reports, single-source intelligence reports, ecosystem health assessment, quick summaries

� Phase VII — Scientific Optimization & Blockchain Anchoring

Advanced mathematical analysis, formal proof generation, Rust smart contracts, and deterministic on-chain anchoring.

%%{init: {'theme': 'dark'}}%%
graph LR
    subgraph MATH["⚪ Mathematical Expansion"]
        MFD[Missing Factor<br/>Detector]
        SO[Solution<br/>Optimizer]
        SA[Stability<br/>Analyzer]
        CRM[Canonical<br/>Reference Map]
        FPE[Formal Proof<br/>Exporter]
    end

    subgraph OPT["⚪ Optimization Metrics"]
        SI[Solvability<br/>Index]
        MES[Model Efficiency<br/>Score]
        CR[Compression<br/>Ratio]
    end

    subgraph PERF["⚪ Performance"]
        AE[Async<br/>Executor]
        CM[Cache<br/>Manager]
        BS[Benchmark<br/>Suite]
    end

    subgraph CHAIN["⚪ Blockchain"]
        RAB[Rust Anchor<br/>Bridge]
        RC[Rust Smart<br/>Contracts]
        REG[Scientific<br/>Registry]
    end

    MATH --> OPT
    OPT --> CHAIN
    PERF --> MATH
    CHAIN --> DB[(SQLite + Chain)]

    style MATH fill:#1a1a1a,stroke:#8b949e,color:#c9d1d9
    style OPT fill:#1a1a1a,stroke:#8b949e,color:#c9d1d9
    style PERF fill:#1a1a1a,stroke:#8b949e,color:#c9d1d9
    style CHAIN fill:#1a1a1a,stroke:#8b949e,color:#c9d1d9

#	Module	Description
37	Missing Factor Detector	Detects omitted physical constants (G, c, ℏ, k_B), dimensional inconsistencies, implicit unit assumptions, canonical deviations
38	Solution Optimizer	8-strategy simplification (expand, factor, cancel, trigsimp…), compression ratio, overparameterization detection
39	Stability Analyzer	Jacobian computation, eigenvalue extraction, Lyapunov exponents, stability classification (7 classes)
40	Canonical Reference Map	15 canonical equations (Newton → Boltzmann), structural/algebraic comparison, closest-match finder
41	Formal Proof Exporter	Step-by-step proof trees, SMT-LIB 2.0 export (QF_NRA logic), axiom tracking, DB persistence
42	Scientific Registry	12 default contributors (Newton → Coulomb), domain filtering, equation/claim linking, SHA-256 hashing
43	Solvability Index	Formula: SI = C/(V+1) × (1-S) × D, stability class mapping, tractability interpretation
44	Model Efficiency Score	Operation count, AST depth, parameter count, normalized cost, efficiency scoring
45	Compression Ratio	Multi-strategy comparison, best strategy selection, equivalence verification
46	Async Executor	ThreadPoolExecutor batch processing, deterministic result ordering, error isolation
47	Cache Manager	SHA-256 keyed LRU cache, TTL expiry, deterministic invalidation, hit/miss statistics
48	Benchmark Suite	Context-manager timing, psutil memory/CPU tracking, JSON log export, DB persistence
49	Rust Anchor Bridge	Payloads for merkle_root/claim_score/equation_proof, dry-run mode, receipt verification
50	Rust Smart Contracts	CosmWasm-compatible contracts: anchor_registry, merkle_anchor, claim_score_anchor, equation_proof_anchor

🟧 CLI Command Reference

87 commands across 7 phases. All invoked via python main.py.

🟢 Phase I — Research & Collection (21 commands)

python main.py --collect              # Scrape social media / web
python main.py --academic             # Search academic databases
python main.py --government           # Search government records
python main.py --analyze-pdf FILE     # Analyze a specific PDF
python main.py --physics              # Run physics computations
python main.py --waves                # Run wave science computations
python main.py --nlp                  # Run narrative NLP analysis
python main.py --graph                # Build propagation graph
python main.py --report               # Generate JSON reports
python main.py --static-report        # Generate HTML report
python main.py --dashboard            # Launch interactive dashboard

# IPFS commands (requires local Kubo node):
python main.py --ipfs-status          # Show IPFS node status
python main.py --ipfs-archive         # Archive all evidence to IPFS
python main.py --ipfs-pin FILE        # Pin a specific file
python main.py --ipfs-verify          # Verify proof chain integrity
python main.py --gateway-health       # Check multi-gateway health

# Extended research:
python main.py --taxonomy             # Load taxonomy knowledge base
python main.py --taxonomy-search TERM # Search taxonomy entries
python main.py --taxonomy-export      # Export taxonomy to JSON
python main.py --arxiv TERM           # Search arXiv
python main.py --extended-search      # Run all extended terms

🔵 Phase II — Cryptographic Integrity (12 commands)

python main.py --key-generate         # Generate Ed25519 signing keypair
python main.py --sign-cid CID        # Sign a CID with default key
python main.py --verify-cid CID      # Verify a CID signature
python main.py --snapshot             # Create Merkle snapshot of database
python main.py --verify-snapshot      # Verify latest Merkle snapshot
python main.py --ipns-publish CID    # Publish CID to IPNS
python main.py --ipns-resolve         # Resolve current IPNS pointer
python main.py --generate-audit       # Generate comprehensive audit report
python main.py --foia-search QUERY   # Search all FOIA sources
python main.py --tesla                # Run Tesla investigation
python main.py --load-scientists      # Load scientist cases database
python main.py --search-scientists Q  # Search scientist cases

🟡 Phase III — Mathematical Framework (11 commands)

python main.py --parse-equation 'E = m*c**2'   # Parse plaintext equation
python main.py --parse-latex '\frac{1}{2}mv^2'  # Parse LaTeX equation
python main.py --dim-check newton_gravity       # Dimensional analysis
python main.py --simplify-eq 'x**2 + 2*x + 1'  # Simplify expression
python main.py --math-audit                     # Full math forensics audit

python main.py --add-claim "claim text"         # Add claim to graph
python main.py --add-source "source title"      # Add source node
python main.py --link-claim 'cid,sid,supports'  # Link claim to source
python main.py --claim-stats                    # Show graph statistics
python main.py --provenance ID                  # Show claim provenance chain
python main.py --contradictions                 # List all contradictions

🟠 Phase IV — Quantitative Scoring (8 commands)

python main.py --score-claim ID       # Bayesian confidence score
python main.py --score-all            # Score all claims, rank results
python main.py --mutation-entropy ID  # Mutation entropy analysis
python main.py --citation-density ID  # Citation density analysis
python main.py --tension-map          # Show contradiction tension map
python main.py --propagation ID       # Track propagation velocity
python main.py --claim-report [ID]    # Full epistemic scoring report
python main.py --quick-score ID       # One-line epistemic summary

🔴 Phase V — Temporal Dynamics (11 commands)

python main.py --conf-snapshot [ID]        # Snapshot confidence (0=all)
python main.py --conf-trend ID             # Confidence trend analysis
python main.py --entropy-snapshot [ID]     # Snapshot entropy (0=all)
python main.py --entropy-trend ID          # Entropy trend analysis
python main.py --drift-kinematics ID       # Drift kinematics analysis
python main.py --classify-claim ID         # Classify stability state
python main.py --classify-all              # Classify all claims
python main.py --alert-scan [ID]           # Scan for anomaly alerts (0=all)
python main.py --alert-list                # List pending alerts
python main.py --lifecycle [ID]            # Lifecycle report (0=system)
python main.py --quick-lifecycle ID        # One-line lifecycle summary

🟣 Phase VI — Source Intelligence (11 commands)

python main.py --source-snapshot [ID]      # Snapshot source reputation (0=all)
python main.py --source-profile ID         # Full reputation profile
python main.py --source-rank               # Rank all sources by reliability
python main.py --influence-build           # Build source influence edges
python main.py --influence-network         # Analyze the influence network
python main.py --coord-scan [WINDOW]       # Scan for coordination (default: 24h)
python main.py --coord-summary             # Coordination detection summary
python main.py --provenance-trace [ID]     # Deep provenance trace (0=all)
python main.py --provenance-summary        # Deep provenance summary
python main.py --source-report [ID]        # Source forensics report (0=ecosystem)
python main.py --quick-source ID           # One-line source intelligence summary

⚪ Phase VII — Scientific Optimization & Blockchain (10 commands)

python main.py --detect-missing 'm*c**2'        # Detect missing factors in equation
python main.py --optimize-equation 'x**2+2*x+1' # Optimize / simplify equation
python main.py --stability-analysis '-x,-2*y'   # Stability analysis (comma-sep system)
python main.py --formal-proof 'm*c**2'           # Generate formal proof tree + SMT-LIB
python main.py --solvability 'm*c**2'            # Compute solvability index
python main.py --efficiency-score 'G*m1*m2/r**2' # Compute model efficiency score
python main.py --scientist-link 'Newton,42'      # Link scientist to claim ID
python main.py --anchor-root HASH                # Anchor Merkle root to blockchain
python main.py --anchor-equation 'm*c**2'        # Anchor equation proof to blockchain
python main.py --benchmark                       # Run performance benchmarks

🟪 Database Schema

39 tables in SQLite WAL mode at data/project_anchor.db

%%{init: {'theme': 'dark'}}%%
erDiagram
    CLAIM_NODES ||--o{ EVIDENCE_LINKS : "linked via"
    SOURCE_NODES ||--o{ EVIDENCE_LINKS : "provides"
    CLAIM_NODES ||--o{ CLAIM_SCORES : "scored by"
    CLAIM_NODES ||--o{ MUTATION_METRICS : "tracked by"
    CLAIM_NODES ||--o{ CONFIDENCE_TIMELINE : "snapshot"
    CLAIM_NODES ||--o{ ENTROPY_TIMELINE : "entropy"
    CLAIM_NODES ||--o{ STABILITY_CLASSIFICATIONS : "classified"
    CLAIM_NODES ||--o{ PROVENANCE_TRACES : "traced"
    SOURCE_NODES ||--o{ SOURCE_REPUTATION : "reputation"
    SOURCE_NODES ||--o{ INFLUENCE_EDGES : "influences"
    CLAIM_NODES ||--o{ COORDINATION_EVENTS : "coordinated"

🟢 Phase I — Collection & Archive (9 tables)

Table	Content
`social_posts`	Scraped social media posts
`documents`	PDF analysis results
`academic_records`	Publication search results
`government_records`	Public record query results
`propagation_edges`	Information spread graph
`physics_comparisons`	Computed physics values
`narrative_patterns`	NLP analysis results
`ipfs_evidence`	IPFS-pinned evidence CIDs & proof chain
`taxonomy_entries`	Taxonomy knowledge base entries

🔵 Phase II — Integrity & Research (7 tables)

Table	Content
`crypto_keys`	Ed25519 signing keypair metadata
`merkle_snapshots`	Merkle tree snapshot records
`foia_documents`	FOIA document records
`investigation_cases`	Investigation case records
`case_claims`	Claims linked to investigation cases
`scientist_cases`	Historical scientist case records
`audit_logs`	Audit trail entries

🟡 Phase III — Mathematical & Graph (6 tables)

Table	Content
`equation_proofs`	Parsed equation metadata & hashes
`derivation_steps`	Step-by-step derivation chain records
`claim_nodes`	Typed claim nodes in evidence graph
`source_nodes`	Source nodes (documents, academic, social)
`evidence_links`	Weighted edges between nodes
`entity_nodes`	Person/organization entity nodes

🟠 Phase IV — Scoring (3 tables)

Table	Content
`claim_scores`	Bayesian confidence score breakdowns
`mutation_metrics`	Shannon entropy & drift velocity metrics
`propagation_events`	Propagation event log (platform, reach, timestamp)

🔴 Phase V — Temporal Dynamics (4 tables)

Table	Content
`confidence_timeline`	Confidence score snapshots over time
`entropy_timeline`	Shannon entropy snapshots over time
`stability_classifications`	Epistemic state classifications
`epistemic_alerts`	Anomaly alerts with severity levels

🟣 Phase VI — Source Intelligence (4 tables)

Table	Content
`source_reputation`	Source reliability snapshots (EMA, accuracy, trend)
`influence_edges`	Source-to-source amplification edges (shared claims, directionality)
`coordination_events`	Detected temporal coordination clusters (scores, patterns)
`provenance_traces`	Deep provenance traces (origin type, chain depth, confidence)

⚪ Phase VII — Scientific Optimization (6 tables)

Table	Content
`scientific_registry`	Contributor records (domain, equations, citations, SHA-256)
`equation_stability`	Jacobian, eigenvalues, Lyapunov exponents, stability class
`equation_optimization`	Original/simplified expressions, compression ratio, missing factors
`formal_proofs`	Proof trees (JSON), SMT-LIB exports, axioms, validity flags
`blockchain_anchors`	Anchor payloads, transaction IDs, on-chain hashes, receipt status
`performance_metrics`	Operation timings, memory/CPU usage, benchmark metadata

All operations are logged to timestamped files in logs/.

🟥 Scoring & Algorithm Reference

Bayesian Confidence Scoring (🟠 Phase IV)

Six-component weighted composite:

C(claim) = w₁·Prior + w₂·Credibility + w₃·Citation + w₄·Contradiction + w₅·Verification + w₆·MutationDecay

Component	Description
Prior	Base probability by claim type (observation, hypothesis, rebuttal)
Source Credibility	Average credibility of linked sources
Citation Support	Cross-reference density and quality weighting
Contradiction Penalty	Log-scaled tension from opposing claims
Verification Bonus	Status-based modifier (confirmed → retracted)
Mutation Decay	Confidence loss through claim text drift

Trajectory Scoring (🔴 Phase V)

Weighted composite score (0–100%) with letter grade:

Component	Weight	Signal
Confidence stability	30%	Low σ across timeline
Entropy stability	25%	Low dH/dt
Drift stability	20%	Low acceleration
Classification bonus	15%	Stable/converging state
Alert penalty	10%	Fewer anomaly flags

Grade scale: A (90+) · B (75+) · C (60+) · D (40+) · F (<40)

Source Reputation Index (🟣 Phase VI)

Four-component weighted reliability index:

R(source) = 0.40·Accuracy + 0.30·EMA + 0.20·Consistency + 0.10·Volume

Component	Weight	Formula
Accuracy rate	40%	`(support + 1) / (total + 2)` — Laplace smoothed
EMA credibility	30%	Exponential moving average, α = 0.3
Consistency	20%	`1 − 3σ` of reliability history
Volume bonus	10%	`log₂(claim_count + 1) / 10`, capped at 1.0

Grade scale: A (≥0.90) · B (≥0.75) · C (≥0.60) · D (≥0.40) · F (<0.40)

Coordination Scoring (🟣 Phase VI)

Three-component coordination score:

S(cluster) = 0.35·CountFactor + 0.40·Tightness + 0.25·DensityFactor

Component	Weight	Formula
Count factor	35%	`log₂(source_count) / log₂(max_expected)`
Tightness	40%	`1 − (time_spread / window_hours)`
Density factor	25%	`sources_per_hour`, capped at 1.0

Pattern types: simultaneous (spread < 1h) · cascade (spread < 30% window) · burst (default)

Ecosystem Health (🟣 Phase VI)

H(ecosystem) = 0.40·Reliability + 0.25·(1 − OrphanRate) + 0.20·Connectivity + 0.15·(1 − MaxCoordScore)

Component	Weight	Description
Mean source reliability	40%	Average reliability index across all sources
Low orphan rate	25%	`1 − (orphan claims / total claims)`
Network connectivity	20%	`1 − fragmentation ratio`
Low coordination suspicion	15%	`1 − highest coordination score`

Provenance Classification (🟣 Phase VI)

Origin Type	Criteria
`original`	No mutation parent, has source links
`derived`	Mutation chain, Jaccard similarity ≥ 0.5
`mutated`	Mutation chain, Jaccard similarity < 0.5
`amplified`	Multiple sources, no mutation parent
`orphan`	No sources, no parent

Confidence decay: 0.85× per chain hop. Max trace depth: 20.

Stability State Machine (🔴 Phase V)

%%{init: {'theme': 'dark'}}%%
stateDiagram-v2
    [*] --> Stable
    Stable --> Converging : variance decreasing
    Stable --> Volatile : σ spike
    Converging --> Stable : plateau reached
    Converging --> Volatile : σ reversal
    Volatile --> Diverging : drift + entropy ↑
    Volatile --> Converging : settling
    Diverging --> Critical : 3+ anomaly flags
    Diverging --> Volatile : drift slows
    Critical --> Volatile : flags resolved
    Critical --> Diverging : partial recovery

State	Description
🟢 Stable	Low variance, consistent metrics across all temporal signals
🔵 Converging	Decreasing variance, narrowing oscillation, approaching plateau
🟡 Volatile	High variance in confidence or entropy, frequent direction changes
🟠 Diverging	Accelerating drift combined with increasing entropy
🔴 Critical	Three or more simultaneous anomaly flags from different subsystems

Alert Types (🔴 Phase V)

Nine categories across three severity levels:

Alert	Severity	Trigger
`entropy_spike`	⚠️ Warning	H(t) exceeds 2σ above mean
`entropy_collapse`	🚨 Critical	H(t) drops below 2σ below mean
`confidence_collapse`	🚨 Critical	C(t) drops below 2σ below mean
`confidence_surge`	⚠️ Warning	C(t) exceeds 2σ above mean
`drift_acceleration`	⚠️ Warning	d²d/dt² exceeds threshold
`drift_inflection`	ℹ️ Info	Sign change in acceleration
`tension_surge`	⚠️ Warning	Contradiction tension spike
`stability_transition`	ℹ️ Info	State machine transition
`critical_state`	🚨 Critical	Claim enters critical state

🟥 Flow Diagrams

Complete Data Pipeline

%%{init: {'theme': 'dark'}}%%
flowchart TB
    START(["🚀 python main.py --all"])

    subgraph COLLECT["🟢 PHASE I: COLLECT"]
        C1["Scrape Reddit\nWayback\nWeb Search"]
        C2["Query Gov DBs\nAcademic DBs"]
        C3["Analyze PDFs"]
        C4["Run Physics\nEngine"]
        C5["NLP Analysis"]
        C6["Pin to IPFS"]
    end

    subgraph SECURE["🔵 PHASE II: SECURE"]
        S1["Sign with\nEd25519"]
        S2["Merkle\nSnapshot"]
        S3["FOIA\nForensics"]
        S4["Generate\nAudit"]
    end

    subgraph MATH["🟡 PHASE III: MODEL"]
        M1["Parse\nEquations"]
        M2["Dimensional\nAnalysis"]
        M3["Build Claim\nGraph"]
    end

    subgraph SCORE["🟠 PHASE IV: SCORE"]
        SC1["Bayesian\nConfidence"]
        SC2["Mutation\nEntropy"]
        SC3["Citation\nDensity"]
        SC4["Contradiction\nMapping"]
    end

    subgraph TEMPORAL["🔴 PHASE V: TRACK"]
        T1["Confidence\nTimeline"]
        T2["Entropy\nTrend"]
        T3["Drift\nKinematics"]
        T4["Classify\nStability"]
        T5["Alert\nScan"]
    end

    subgraph INTEL["🟣 PHASE VI: INTELLIGENCE"]
        I1["Source\nReputation"]
        I2["Influence\nNetwork"]
        I3["Coordination\nDetection"]
        I4["Deep\nProvenance"]
        I5["Forensics\nReport"]
    end

    FINISH(["📊 Reports & Dashboard"])

    START --> COLLECT
    COLLECT --> SECURE
    SECURE --> MATH
    MATH --> SCORE
    SCORE --> TEMPORAL
    TEMPORAL --> INTEL
    INTEL --> FINISH

    style START fill:#0d1117,stroke:#58a6ff,color:#58a6ff
    style COLLECT fill:#0d2818,stroke:#2ea043,color:#7ee787
    style SECURE fill:#0d1b2e,stroke:#58a6ff,color:#79c0ff
    style MATH fill:#2e2a0d,stroke:#d29922,color:#e3b341
    style SCORE fill:#2e1a0d,stroke:#f0883e,color:#ffa657
    style TEMPORAL fill:#2e0d0d,stroke:#f85149,color:#ff7b72
    style INTEL fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style FINISH fill:#0d1117,stroke:#58a6ff,color:#58a6ff

Scoring Cascade Flow

%%{init: {'theme': 'dark'}}%%
flowchart LR
    subgraph RAW["Raw Signals"]
        R1["Source Links"]
        R2["Claim Text"]
        R3["Timestamps"]
        R4["Contradictions"]
    end

    subgraph P4["🟠 Phase IV Scores"]
        S1["Bayesian\nConfidence"]
        S2["Shannon\nEntropy"]
        S3["Citation\nDensity"]
        S4["Tension\nMap"]
    end

    subgraph P5["🔴 Phase V Dynamics"]
        D1["dC/dt"]
        D2["dH/dt · d²H/dt²"]
        D3["Velocity · Accel\nJerk"]
        D4["State\nClassification"]
    end

    subgraph P6["🟣 Phase VI Intel"]
        I1["Source\nReliability"]
        I2["Network\nCentrality"]
        I3["Coordination\nScore"]
        I4["Provenance\nChain"]
    end

    VERDICT["📊 Epistemic\nVerdict"]

    R1 & R2 & R3 & R4 --> P4
    P4 --> P5
    P5 --> P6
    P6 --> VERDICT

    style RAW fill:#161b22,stroke:#8b949e,color:#c9d1d9
    style P4 fill:#2e1a0d,stroke:#f0883e,color:#ffa657
    style P5 fill:#2e0d0d,stroke:#f85149,color:#ff7b72
    style P6 fill:#1f0d2e,stroke:#a371f7,color:#d2a8ff
    style VERDICT fill:#0d1117,stroke:#58a6ff,color:#58a6ff

⬜ Testing

390 tests · 10 test suites · All passing

%%{init: {'theme': 'dark'}}%%
pie title Test Distribution by Phase (390 total)
    "🟢 Phase I : 9" : 9
    "🔵 Phase II : 24" : 24
    "🟡 Phase III : 34" : 34
    "🟠 Phase IV : 42" : 42
    "🔴 Phase V : 75" : 75
    "🟣 Phase VI : 100" : 100
    "⚪ Phase VII : 106" : 106

# Run full suite
python -m pytest tests/ -v                             # 390 tests

# Run by phase
python -m pytest tests/test_physics.py -v              # 🟢   9 tests — Physics engine
python -m pytest tests/test_phase2.py -v               # 🔵  24 tests — Crypto & integrity
python -m pytest tests/test_phase3.py -v               # 🟡  34 tests — Math & claim graph
python -m pytest tests/test_phase4.py -v               # 🟠  42 tests — Scoring engine
python -m pytest tests/test_phase5.py -v               # 🔴  75 tests — Temporal dynamics
python -m pytest tests/test_phase6.py -v               # 🟣 100 tests — Source intelligence
python -m pytest tests/test_phase7_math.py -v          # ⚪  40 tests — Math expansion
python -m pytest tests/test_phase7_anchor.py -v        # ⚪  14 tests — Blockchain anchoring
python -m pytest tests/test_phase7_registry.py -v      # ⚪  18 tests — Scientific registry
python -m pytest tests/test_phase7_performance.py -v   # ⚪  34 tests — Performance & optimization

All tests use :memory: SQLite via PROJECT_ANCHOR_DB environment variable.

⬜ IPFS Integration

The system integrates with a local IPFS Kubo node for immutable, content-addressed evidence storage:

%%{init: {'theme': 'dark'}}%%
flowchart LR
    subgraph IPFS_FLOW["IPFS Evidence Pipeline"]
        A["📄 Evidence\nDocument"] --> B["📌 Pin to\nIPFS"]
        B --> C["🔗 Get CID"]
        C --> D["⛓️ Link to\nProof Chain"]
        D --> E["✍️ Ed25519\nSign CID"]
        E --> F["📢 Publish\nto IPNS"]
    end

    style IPFS_FLOW fill:#0d1117,stroke:#65c2cb,color:#65c2cb

Feature	Description
Proof Chain	Each evidence item pinned to IPFS and linked into a DAG chain with tamper-evident CID references
Content Addressing	Every item gets a CID — a cryptographic hash. Any byte change produces a new CID
SHA-256 Verification	Independent SHA-256 hashes stored alongside CIDs for double verification
IPNS Publishing	Chain head published to IPNS for a stable, updatable reference
Multi-Gateway	Health checking and pinning across multiple IPFS gateways

Requirements

Component	Endpoint
IPFS Kubo (desktop or daemon)	Running locally
RPC API	`http://127.0.0.1:5001`
Gateway	`http://127.0.0.1:8081`

Workflow

python main.py --ipfs-status          # 1. Check node is online
python main.py --all                  # 2. Run research pipeline
python main.py --ipfs-archive         # 3. Archive everything to IPFS
python main.py --ipfs-verify          # 4. Verify proof chain integrity
python main.py --ipfs-pin doc.pdf     # 5. Pin a specific document

⬜ Operational Scope & Reproducibility

Scope

This system is limited to:

✅ Publicly accessible data
✅ Public records & open-source intelligence
✅ Public academic databases

It does not:

❌ Access classified systems
❌ Bypass encryption
❌ Access restricted government networks

Reproducibility Guarantees

Guarantee	Mechanism
Source citation	All sources cited with URLs and timestamps
Audit trail	All operations logged with full audit trail
Portability	Self-contained SQLite database
Documentation	Physics equations and constants documented inline
Tamper evidence	Cryptographic signatures on all evidence
Integrity verification	Merkle snapshots verify database state
No API keys required	Basic operation works without external keys

⬜ Quick Start

# Clone repository
git clone https://github.com/FTHTrading/Gravity-.git
cd Gravity-

# Install dependencies
pip install -r requirements.txt

# Initialize database
python main.py --init-db

# Run complete pipeline
python main.py --all

# Check system status
python main.py --claim-stats

⬜ Dependencies

Python 3.11+ with:

Package	Purpose
`requests`	HTTP client for API calls
`PyMuPDF`	PDF parsing and metadata extraction
`pdfminer.six`	PDF text extraction
`nltk`	Natural language processing
`scikit-learn`	TF-IDF vectorization, cosine similarity
`networkx`	Graph analysis, centrality, PageRank
`matplotlib`	Static chart generation
`plotly`	Interactive visualizations
`dash`	Web dashboard framework
`python-dateutil`	Date parsing and manipulation
`cryptography`	Ed25519 signatures (v46.0+)
`sympy`	Symbolic mathematics CAS (v1.14+)
`pytest`	Test framework

_{Built with forensic precision. Every claim tracked. Every source measured. Every change recorded.}

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
.github/workflows		.github/workflows
contracts		contracts
docs		docs
k8s		k8s
scaling		scaling
src		src
tests		tests
.gitignore		.gitignore
Dockerfile		Dockerfile
README.md		README.md
docker-compose.yml		docker-compose.yml
main.py		main.py
requirements-scale.txt		requirements-scale.txt
requirements.txt		requirements.txt

Folders and files

Latest commit

History

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GRAVITY-

Case File

Table of Contents

🟦 System Architecture

🟦 Phase Pipeline Overview

Capability Matrix

Phase Statistics

Phase Dependency Flow

🟦 Repository Structure

🟩 Phase I — Research & Collection

🟩 Phase II — Cryptographic Integrity

🟩 Phase III — Mathematical Framework

🟩 Phase IV — Quantitative Scoring

🟩 Phase V — Temporal Dynamics

🟩 Phase VI — Source Intelligence & Network Forensics

� Phase VII — Scientific Optimization & Blockchain Anchoring

🟧 CLI Command Reference

🟪 Database Schema

🟥 Scoring & Algorithm Reference

Bayesian Confidence Scoring (🟠 Phase IV)

Trajectory Scoring (🔴 Phase V)

Source Reputation Index (🟣 Phase VI)

Coordination Scoring (🟣 Phase VI)

Ecosystem Health (🟣 Phase VI)

Provenance Classification (🟣 Phase VI)

Stability State Machine (🔴 Phase V)

Alert Types (🔴 Phase V)

🟥 Flow Diagrams

Complete Data Pipeline

Scoring Cascade Flow

⬜ Testing

⬜ IPFS Integration

Requirements

Workflow

⬜ Operational Scope & Reproducibility

Scope

Reproducibility Guarantees

⬜ Quick Start

⬜ Dependencies

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