Changes for page Requirements

Last modified by Robert Schaub on 2026/02/08 21:32

From 5.1 to 6.1 From 7.3 to 7.4

From version 6.1

edited by Robert Schaub
on 2025/12/23 08:03

Change comment: Imported from XAR

To version 7.3

edited by Robert Schaub
on 2026/01/20 20:22

Change comment: Renamed back-links.

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Page properties (2 modified, 0 added, 0 removed)

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Page properties

Parent

@@ -1,1 +1,1 @@
--Test.FactHarbor.Specification.WebHome
++Test.FactHarbor V0\.9\.76.Specification.WebHome

Content

@@ -32,6 +32,7 @@
  **Who**: Anyone (no login required)
  **Can**:
++
  * Browse and search claims
  * View scenarios, evidence, verdicts, and confidence scores
  * Flag issues or errors
@@ -39,6 +39,7 @@
  * Submit claims automatically (new claims added if not duplicates)
  **Cannot**:
++
  * Modify content
  * Access edit history details
@@ -49,6 +49,7 @@
  **Who**: Registered users (earns reputation through contributions)
  **Can**:
++
  * Everything a Reader can do
  * Edit claims, evidence, and scenarios
  * Add sources and citations
@@ -57,6 +57,7 @@
  * Earn reputation points for quality contributions
  **Reputation System**:
++
  * New contributors: Limited edit privileges
  * Established contributors (established reputation): Full edit access
  * Trusted contributors (substantial reputation): Can approve certain changes
@@ -64,6 +64,7 @@
  * Reputation lost through: Reverted edits, invalid flags, abuse
  **Cannot**:
++
  * Delete or hide content (only moderators)
  * Override moderation decisions
@@ -74,6 +74,7 @@
  **Who**: Trusted community members with proven track record, appointed by governance board
  **Can**:
++
  * Review flagged content
  * Hide harmful or abusive content
  * Resolve disputes between contributors
@@ -82,6 +82,7 @@
  * Access full audit logs
  **Cannot**:
++
  * Change governance rules
  * Permanently ban users without board approval
  * Override technical quality gates
@@ -95,6 +95,7 @@
  **Not a permanent role**: Contacted externally when needed for contested claims in their domain
  **When used**:
++
  * Medical claims with life/safety implications
  * Legal interpretations with significant impact
  * Scientific claims with high controversy
@@ -101,6 +101,7 @@
  * Technical claims requiring specialized knowledge
  **Process**:
++
  * Moderator identifies need for expert input
  * Contact expert externally (don't require them to be users)
  * Trusted Contributor provides written opinion with sources
@@ -120,11 +120,13 @@
  **Status**: Visible to all users
  **Includes**:
++
  * AI-generated analyses (default state)
  * User-contributed content
  * Edited/improved content
  **Quality Indicators** (displayed with content):
++
  * **Confidence Score**: 0-100% (AI's confidence in analysis)
  * **Source Quality Score**: 0-100% (based on source track record)
  * **Controversy Flag**: If high dispute/edit activity
@@ -134,6 +134,7 @@
  * **Review Status**: AI-generated / Human-reviewed / Expert-validated
  **Automatic Warnings**:
++
  * Confidence < 60%: "Low confidence - use caution"
  * Source quality < 40%: "Sources may be unreliable"
  * High controversy: "Disputed - multiple interpretations exist"
@@ -146,6 +146,7 @@
  **Status**: Not visible to regular users (only to moderators)
  **Reasons**:
++
  * Spam or advertising
  * Personal attacks or harassment
  * Illegal content
@@ -154,6 +154,7 @@
  * Abuse or harmful content
  **Process**:
++
  * Automated detection flags for moderator review
  * Moderator confirms and hides
  * Original author notified with reason
@@ -176,6 +176,7 @@
  **AKEL is the primary system**. Human contributions supplement and train AKEL.
  **AKEL Must**:
++
  * Mark all outputs as AI-generated
  * Display confidence scores prominently
  * Provide source citations
@@ -184,6 +184,7 @@
  * Learn from human corrections
  **When AKEL Makes Errors**:
++
 . Capture the error pattern (what, why, how common)
 . Improve the system (better prompt, model, validation)
 . Re-process affected claims automatically
@@ -214,6 +214,7 @@
  === 4.1 Source Requirements ===
  **Track Record Over Credentials**:
++
  * Sources evaluated by historical accuracy
  * Correction policy matters
  * Independence from conflicts of interest
@@ -220,6 +220,7 @@
  * Methodology transparency
  **Source Quality Database**:
++
  * Automated tracking of source accuracy
  * Correction frequency
  * Reliability score (updated continuously)
@@ -251,6 +251,7 @@
  === 4.4 Confidence Scoring ===
  **Automated confidence calculation based on**:
++
  * Source quality scores
  * Evidence consistency
  * Contradiction detection
@@ -258,6 +258,7 @@
  * Historical accuracy of similar claims
  **Thresholds**:
++
  * < 40%: Too low to publish (needs improvement)
  * 40-60%: Published with "Low confidence" warning
  * 60-80%: Published as standard
@@ -274,6 +274,7 @@
  === 5.1 Risk Score Calculation ===
  **Factors** (weighted algorithm):
++
  * **Domain sensitivity**: Medical, legal, safety auto-flagged higher
  * **Potential impact**: Views, citations, spread
  * **Controversy level**: Flags, disputes, edit wars
@@ -300,6 +300,7 @@
  === 6.1 Error Capture ===
  **When users flag errors or make corrections**:
++
 . What was wrong? (categorize)
 . What should it have been?
 . Why did the system fail? (root cause)
@@ -318,6 +318,7 @@
  === 6.3 Quality Metrics Dashboard ===
  **Track continuously**:
++
  * Error rate by category
  * Source quality distribution
  * Confidence score trends
@@ -343,6 +343,7 @@
  === 7.2 Anomaly Detection ===
  **Automated alerts for**:
++
  * Sudden quality drops
  * Unusual patterns
  * Contradiction clusters
@@ -395,6 +395,7 @@
  **Fulfills**: UN-2 (Context-dependent verification), UN-3 (Article summary with FactHarbor analysis summary), UN-8 (Understanding disagreement)
  **Automated scenario creation**:
++
  * AKEL analyzes claim and generates likely scenarios (use-cases and contexts)
  * Each scenario includes: assumptions, definitions, boundaries, evidence context
  * Users can flag incorrect scenarios
@@ -455,6 +455,7 @@
  **Purpose**: Provide side-by-side comparison of what a document claims vs. FactHarbor's complete analysis of its credibility
  **Left Panel: Article Summary**:
++
  * Document title, source, and claimed credibility
  * "The Big Picture" - main thesis or position change
  * "Key Findings" - structured summary of document's main claims
@@ -462,6 +462,7 @@
  * "Conclusion" - document's bottom line
  **Right Panel: FactHarbor Analysis Summary**:
++
  * FactHarbor's independent source credibility assessment
  * Claim-by-claim verdicts with confidence scores
  * Methodology assessment (strengths, limitations)
@@ -469,6 +469,7 @@
  * Analysis ID for reference
  **Design Principles**:
++
  * No scrolling required - both panels visible simultaneously
  * Visual distinction between "what they say" and "FactHarbor's analysis"
  * Color coding for verdicts (supported, uncertain, refuted)
@@ -476,6 +476,7 @@
  * Mobile responsive (panels stack vertically on small screens)
  **Implementation Notes**:
++
  * Generated automatically by AKEL for every analyzed document
  * Updates when verdict evolves (maintains version history)
  * Exportable as standalone summary report
@@ -502,7 +502,8 @@
  (% style="font-size:0.9em; color:#666;" %)
  ↑ WELL SUPPORTED • 87% confidence
  [[Click for evidence details →]]
--(%%)
++
++
  )))
  The study, which followed 10,000 participants over five years, showed significant improvements in cardiovascular health markers.
@@ -515,7 +515,8 @@
  ↑ UNCERTAIN • 45% confidence
  Overstated - evidence shows risk reduction, not prevention
  [[Click for details →]]
--(%%)
++
++
  )))
  Dr. Maria Rodriguez, lead researcher, recommends incorporating more olive oil, fish, and vegetables into daily meals.
@@ -528,7 +528,8 @@
  ↑ REFUTED • 15% confidence
  Claim not supported by study design; correlation ≠ causation
  [[Click for counter-evidence →]]
--(%%)
++
++
  )))
  Participants also reported feeling more energetic and experiencing better sleep quality, though these were secondary measures.
@@ -535,6 +535,7 @@
  )))
  **Legend:**
++
  * 🟢 = Well-supported claim (confidence ≥75%)
  * 🟡 = Uncertain claim (confidence 40-74%)
  * 🔴 = Refuted/unsupported claim (confidence <40%)
@@ -553,11 +553,13 @@
  **Confidence:** 87%
  **Evidence Summary:**
++
  * Meta-analysis of 12 RCTs confirms 23-28% risk reduction
  * Consistent findings across multiple populations
  * Published in peer-reviewed journal (high credibility)
  **Uncertainty Factors:**
++
  * Exact percentage varies by study (20-30% range)
  [[View Full Analysis →]]
@@ -564,6 +564,7 @@
  )))
  **Color-Coding System**:
++
  * **Green**: Well-supported claims (confidence ≥75%, strong evidence)
  * **Yellow/Orange**: Uncertain claims (confidence 40-74%, conflicting or limited evidence)
  * **Red**: Refuted or unsupported claims (confidence <40%, contradicted by evidence)
@@ -573,8 +573,12 @@
  (% style="width:100%; border-collapse:collapse;" %)
  |=**Article Text**|=**Status**|=**Analysis**
--|(((A recent study published in the Journal of Nutrition has revealed new findings about the Mediterranean diet.)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Context - no highlighting
--|(((//Researchers found that Mediterranean diet followers had a 25% lower risk of heart disease compared to control groups//)))|(% style="background-color:#D4EDDA; text-align:center; padding:8px;" %)🟢 **WELL SUPPORTED**|(((
++|(((
++A recent study published in the Journal of Nutrition has revealed new findings about the Mediterranean diet.
++)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Context - no highlighting
++|(((
++//Researchers found that Mediterranean diet followers had a 25% lower risk of heart disease compared to control groups//
++)))|(% style="background-color:#D4EDDA; text-align:center; padding:8px;" %)🟢 **WELL SUPPORTED**|(((
  **87% confidence**
  Meta-analysis of 12 RCTs confirms 23-28% risk reduction
@@ -581,8 +581,12 @@
  [[View Full Analysis]]
  )))
--|(((The study, which followed 10,000 participants over five years, showed significant improvements in cardiovascular health markers.)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Methodology - no highlighting
--|(((//Some experts believe this diet can completely prevent heart attacks//)))|(% style="background-color:#FFF3CD; text-align:center; padding:8px;" %)🟡 **UNCERTAIN**|(((
++|(((
++The study, which followed 10,000 participants over five years, showed significant improvements in cardiovascular health markers.
++)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Methodology - no highlighting
++|(((
++//Some experts believe this diet can completely prevent heart attacks//
++)))|(% style="background-color:#FFF3CD; text-align:center; padding:8px;" %)🟡 **UNCERTAIN**|(((
  **45% confidence**
  Overstated - evidence shows risk reduction, not prevention
@@ -589,8 +589,12 @@
  [[View Details]]
  )))
--|(((Dr. Rodriguez recommends incorporating more olive oil, fish, and vegetables into daily meals.)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Recommendation - no highlighting
--|(((//The study proves that saturated fats cause heart disease//)))|(% style="background-color:#F8D7DA; text-align:center; padding:8px;" %)🔴 **REFUTED**|(((
++|(((
++Dr. Rodriguez recommends incorporating more olive oil, fish, and vegetables into daily meals.
++)))|(% style="text-align:center;" %)Plain text|(% style="font-style:italic; color:#888;" %)Recommendation - no highlighting
++|(((
++//The study proves that saturated fats cause heart disease//
++)))|(% style="background-color:#F8D7DA; text-align:center; padding:8px;" %)🔴 **REFUTED**|(((
  **15% confidence**
  Claim not supported by study; correlation ≠ causation
@@ -599,6 +599,7 @@
  )))
  **Design Notes:**
++
  * Highlighted claims use italics to distinguish from plain text
  * Color backgrounds match XWiki message box colors (success/warning/error)
  * Status column shows verdict prominently
@@ -605,6 +605,7 @@
  * Analysis column provides quick summary with link to details
  **User Actions**:
++
  * **Hover** over highlighted claim → Tooltip appears
  * **Click** highlighted claim → Detailed analysis modal/panel
  * **Toggle** button to turn highlighting on/off
@@ -611,16 +611,18 @@
  * **Keyboard**: Tab through highlighted claims
  **Interaction Design**:
++
  * Hover/click on highlighted claim → Show tooltip with:
--  * Claim text
--  * Verdict (e.g., "WELL SUPPORTED")
--  * Confidence score (e.g., "85%")
--  * Brief evidence summary
--  * Link to detailed analysis
++* Claim text
++* Verdict (e.g., "WELL SUPPORTED")
++* Confidence score (e.g., "85%")
++* Brief evidence summary
++* Link to detailed analysis
  * Toggle highlighting on/off (user preference)
  * Adjustable color intensity for accessibility
  **Technical Requirements**:
++
  * Real-time highlighting as page loads (non-blocking)
  * Claim boundary detection (start/end of assertion)
  * Handle nested or overlapping claims
@@ -628,16 +628,19 @@
  * Work with various content formats (HTML, plain text, PDFs)
  **Performance Requirements**:
++
  * Highlighting renders within 500ms of page load
  * No perceptible delay in reading experience
  * Efficient DOM manipulation (avoid reflows)
  **Accessibility**:
++
  * Color-blind friendly palette (use patterns/icons in addition to color)
  * Screen reader compatible (ARIA labels for claim credibility)
  * Keyboard navigation to highlighted claims
  **Implementation Notes**:
++
  * Claims extracted and analyzed by AKEL during initial processing
  * Highlighting data stored as annotations with byte offsets
  * Client-side rendering of highlights based on verdict data
@@ -650,6 +650,7 @@
  **Fulfills**: UN-1 (Fast access to verified content), UN-16 (Clear review status)
  **Simple flow**:
++
 . Claim submitted
 . AKEL processes (automated)
 . If confidence > threshold: Publish (labeled as AI-generated)
@@ -661,6 +661,7 @@
  ==== FR10 — Moderation ====
  **Focus on abuse, not routine quality**:
++
  * Automated abuse detection
  * Moderators handle flags
  * Quick response to harmful content
@@ -731,6 +731,7 @@
  **Purpose:** Ensure extracted claims are factual assertions (not opinions/predictions)
  **Checks:**
++
 . **Factual Statement Test:** Is this verifiable? (Yes/No)
 . **Opinion Detection:** Contains hedging language? ("I think", "probably", "best")
 . **Future Prediction Test:** Makes claims about future events?
@@ -737,6 +737,7 @@
 . **Specificity Score:** Contains specific entities, numbers, dates?
  **Thresholds:**
++
  * Factual: Must be "Yes"
  * Opinion markers: <2 hedging phrases
  * Specificity: ≥3 specific elements
@@ -748,11 +748,13 @@
  **Purpose:** Ensure AI-linked evidence actually relates to claim
  **Checks:**
++
 . **Semantic Similarity Score:** Evidence vs. claim (embeddings)
 . **Entity Overlap:** Shared people/places/things?
 . **Topic Relevance:** Discusses claim subject?
  **Thresholds:**
++
  * Similarity: ≥0.6 (cosine similarity)
  * Entity overlap: ≥1 shared entity
  * Topic relevance: ≥0.5
@@ -764,11 +764,13 @@
  **Purpose:** Validate scenario assumptions are logical and complete
  **Checks:**
++
 . **Completeness:** All required fields populated
 . **Internal Consistency:** Assumptions don't contradict
 . **Distinguishability:** Scenarios meaningfully different
  **Thresholds:**
++
  * Required fields: 100%
  * Contradiction score: <0.3
  * Scenario similarity: <0.8
@@ -780,6 +780,7 @@
  **Purpose:** Only publish high-confidence verdicts
  **Checks:**
++
 . **Evidence Count:** Minimum 2 sources
 . **Source Quality:** Average reliability ≥0.6
 . **Evidence Agreement:** Supporting vs. contradicting ≥0.6
@@ -786,6 +786,7 @@
 . **Uncertainty Factors:** Hedging in reasoning
  **Confidence Tiers:**
++
  * **HIGH (80-100%):** ≥3 sources, ≥0.7 quality, ≥80% agreement
  * **MEDIUM (50-79%):** ≥2 sources, ≥0.6 quality, ≥60% agreement
  * **LOW (0-49%):** <2 sources OR low quality/agreement
@@ -792,11 +792,13 @@
  * **INSUFFICIENT:** <2 sources → DO NOT PUBLISH
  **Implementation Phases:**
++
  * **POC1:** Gates 1 & 4 only (basic validation)
  * **POC2:** All 4 gates (complete framework)
  * **V1.0:** Hardened with <5% hallucination rate
  **Acceptance Criteria:**
++
  * ✅ All gates operational
  * ✅ Hallucination rate <5%
  * ✅ Quality metrics public
@@ -814,6 +814,7 @@
  ==== API Security ====
  **Rate Limiting:**
++
  * **Analysis endpoints:** 100 requests/hour per IP
  * **Read endpoints:** 1,000 requests/hour per IP
  * **Search:** 500 requests/hour per IP
@@ -821,21 +821,24 @@
  * **Burst protection:** Max 10 requests/second
  **Authentication & Authorization:**
++
  * **API Keys:** Required for programmatic access
  * **JWT tokens:** For user sessions (1-hour expiry)
  * **OAuth2:** For third-party integrations
  * **Role-Based Access Control (RBAC):**
--  * Public: Read-only access to published claims
--  * Contributor: Submit claims, provide evidence
--  * Moderator: Review contributions, manage quality
--  * Admin: System configuration, user management
++* Public: Read-only access to published claims
++* Contributor: Submit claims, provide evidence
++* Moderator: Review contributions, manage quality
++* Admin: System configuration, user management
  **CORS Policies:**
++
  * Whitelist approved domains only
  * No wildcard origins in production
  * Credentials required for sensitive endpoints
  **Input Sanitization:**
++
  * Validate all user input against schemas
  * Sanitize HTML/JavaScript in text submissions
  * Prevent SQL injection (use parameterized queries)
@@ -843,11 +843,12 @@
  * Max request size: 10MB
  * File upload restrictions: Whitelist file types, scan for malware
-----
++-----
  ==== Data Security ====
  **Encryption at Rest:**
++
  * Database encryption using AES-256
  * Encrypted backups
  * Key management via cloud provider KMS (AWS KMS, Google Cloud KMS)
@@ -854,6 +854,7 @@
  * Regular key rotation (90-day cycle)
  **Encryption in Transit:**
++
  * HTTPS/TLS 1.3 only (no TLS 1.0/1.1)
  * Strong cipher suites only
  * HSTS (HTTP Strict Transport Security) enabled
@@ -860,6 +860,7 @@
  * Certificate pinning for mobile apps
  **Secure Credential Storage:**
++
  * Passwords hashed with bcrypt (cost factor 12+)
  * API keys encrypted in database
  * Secrets stored in environment variables (never in code)
@@ -866,12 +866,13 @@
  * Use secrets manager (AWS Secrets Manager, HashiCorp Vault)
  **Data Privacy:**
++
  * Minimal data collection (privacy by design)
  * User data deletion on request (GDPR compliance)
  * PII encryption in database
  * Anonymize logs (no PII in log files)
-----
++-----
  ==== Application Security ====
@@ -889,6 +889,7 @@
 . **Server-Side Request Forgery:** URL validation, whitelist domains
  **Security Headers:**
++
  * `Content-Security-Policy`: Strict CSP to prevent XSS
  * `X-Frame-Options`: DENY (prevent clickjacking)
  * `X-Content-Type-Options`: nosniff
@@ -896,6 +896,7 @@
  * `Permissions-Policy`: Restrict browser features
  **Dependency Vulnerability Scanning:**
++
  * **Tools:** Snyk, Dependabot, npm audit, pip-audit
  * **Frequency:** Daily automated scans
  * **Action:** Patch critical vulnerabilities within 24 hours
@@ -902,30 +902,34 @@
  * **Policy:** No known high/critical CVEs in production
  **Security Audits:**
++
  * **Internal:** Quarterly security reviews
  * **External:** Annual penetration testing by certified firm
  * **Bug Bounty:** Public bug bounty program (V1.1+)
  * **Compliance:** SOC 2 Type II certification target (V1.5)
-----
++-----
  ==== Operational Security ====
  **DDoS Protection:**
++
  * CloudFlare or AWS Shield
  * Rate limiting at CDN layer
  * Automatic IP blocking for abuse patterns
  **Monitoring & Alerting:**
++
  * Real-time security event monitoring
  * Alerts for:
--  * Failed login attempts (>5 in 10 minutes)
--  * API abuse patterns
--  * Unusual data access patterns
--  * Security scan detections
++* Failed login attempts (>5 in 10 minutes)
++* API abuse patterns
++* Unusual data access patterns
++* Security scan detections
  * Integration with SIEM (Security Information and Event Management)
  **Incident Response:**
++
  * Documented incident response plan
  * Security incident classification (P1-P4)
  * On-call rotation for security issues
@@ -933,16 +933,18 @@
  * Public disclosure policy (coordinated disclosure)
  **Backup & Recovery:**
++
  * Daily encrypted backups
  * 30-day retention period
  * Tested recovery procedures (quarterly)
  * Disaster recovery plan (RTO: 4 hours, RPO: 1 hour)
-----
++-----
  ==== Compliance & Standards ====
  **GDPR Compliance:**
++
  * User consent management
  * Right to access data
  * Right to deletion
@@ -950,6 +950,7 @@
  * Privacy policy published
  **Accessibility:**
++
  * WCAG 2.1 AA compliance
  * Screen reader compatibility
  * Keyboard navigation
@@ -956,6 +956,7 @@
  * Alt text for images
  **Browser Support:**
++
  * Modern browsers only (Chrome/Edge/Firefox/Safari latest 2 versions)
  * No IE11 support
@@ -970,7 +970,6 @@
  * ✅ Incident response plan documented and tested
  * ✅ 95% uptime over 30-day period
--
  === NFR13: Quality Metrics Transparency ===
  **Fulfills:** User trust, transparency, continuous improvement, IFCN methodology transparency
@@ -985,16 +985,18 @@
  **Core Metrics to Display:**
--**1. Verdict Quality Metrics**
++*
++**
++**1. Verdict Quality Metrics
  **TIGERScore (Fact-Checking Quality):**
++
  * **Definition:** Measures how well generated verdicts match expert fact-checker judgments
  * **Scale:** 0-100 (higher is better)
  * **Calculation:** Using TIGERScore framework (Truth-conditional accuracy, Informativeness, Generality, Evaluativeness, Relevance)
  * **Target:** Average ≥80 for production release
  * **Display:**
--{{code}}
--Verdict Quality (TIGERScore):
++{{code}}Verdict Quality (TIGERScore):
  Overall: 84.2 ▲ (+2.1 from last month)
  Distribution:
@@ -1002,19 +1002,18 @@
    Good (60-80): 28%
    Needs Improvement (<60): 5%
--Trend: [Graph showing improvement over time]
--{{/code}}
++Trend: [Graph showing improvement over time]{{/code}}
  **2. Hallucination & Faithfulness Metrics**
  **AlignScore (Faithfulness to Evidence):**
++
  * **Definition:** Measures how well verdicts align with actual evidence content
  * **Scale:** 0-1 (higher is better)
  * **Purpose:** Detect AI hallucinations (making claims not supported by evidence)
  * **Target:** Average ≥0.85, hallucination rate <5%
  * **Display:**
--{{code}}
--Evidence Faithfulness (AlignScore):
++{{code}}Evidence Faithfulness (AlignScore):
  Average: 0.87 ▼ (-0.02 from last month)
  Hallucination Rate: 4.2%
@@ -1021,24 +1021,24 @@
    - Claims without evidence support: 3.1%
    - Misrepresented evidence: 1.1%
--Action: Prompt engineering review scheduled
--{{/code}}
++Action: Prompt engineering review scheduled{{/code}}
  **3. Evidence Quality Metrics**
  **Source Reliability:**
++
  * Average source quality score (0-1 scale)
  * Distribution of high/medium/low quality sources
  * Publisher track record trends
  **Evidence Coverage:**
++
  * Average number of sources per claim
  * Percentage of claims with ≥2 sources (EFCSN minimum)
  * Geographic diversity of sources
  **Display:**
--{{code}}
--Evidence Quality:
++{{code}}Evidence Quality:
  Average Sources per Claim: 4.2
  Claims with ≥2 sources: 94% (EFCSN compliant)
@@ -1048,24 +1048,23 @@
    Medium quality (0.5-0.8): 43%
    Low quality (<0.5): 9%
--Geographic Diversity: 23 countries represented
--{{/code}}
++Geographic Diversity: 23 countries represented{{/code}}
  **4. Contributor Consensus Metrics** (when human reviewers involved)
  **Inter-Rater Reliability (IRR):**
++
  * **Calculation:** Cohen's Kappa or Fleiss' Kappa for multiple raters
  * **Scale:** 0-1 (higher is better)
  * **Interpretation:**
--  * >0.8: Almost perfect agreement
--  * 0.6-0.8: Substantial agreement
--  * 0.4-0.6: Moderate agreement
--  * <0.4: Poor agreement
++* >0.8: Almost perfect agreement
++* 0.6-0.8: Substantial agreement
++* 0.4-0.6: Moderate agreement
++* <0.4: Poor agreement
  * **Target:** Maintain ≥0.7 (substantial agreement)
  **Display:**
--{{code}}
--Contributor Consensus:
++{{code}}Contributor Consensus:
  Inter-Rater Reliability (IRR): 0.73 (Substantial agreement)
    - Verdict agreement: 78%
@@ -1073,10 +1073,9 @@
    - Scenario structure agreement: 69%
  Cases requiring moderator review: 12
--Moderator override rate: 8%
--{{/code}}
++Moderator override rate: 8%{{/code}}
-----
++-----
  ==== Quality Dashboard Implementation ====
@@ -1083,6 +1083,7 @@
  **Dashboard Location:** `/quality-metrics`
  **Update Frequency:**
++
  * **POC2:** Weekly manual updates
  * **Beta 0:** Daily automated updates
  * **V1.0:** Real-time metrics (updated hourly)
@@ -1132,7 +1132,7 @@
  {{/code}}
-----
++-----
  ==== Continuous Improvement Feedback Loop ====
@@ -1139,31 +1139,36 @@
  **How Metrics Inform AKEL Improvements:**
 . **Identify Weak Areas:**
--   * Low TIGERScore → Review prompt engineering
--   * High hallucination → Strengthen evidence grounding
--   * Low IRR → Clarify evaluation criteria
++* Low TIGERScore → Review prompt engineering
++* High hallucination → Strengthen evidence grounding
++* Low IRR → Clarify evaluation criteria
++
 . **A/B Testing Integration:**
--   * Test prompt variations
--   * Measure impact on quality metrics
--   * Deploy winners automatically
++* Test prompt variations
++* Measure impact on quality metrics
++* Deploy winners automatically
++
 . **Alert Thresholds:**
--   * TIGERScore drops below 75 → Alert team
--   * Hallucination rate exceeds 7% → Pause auto-publishing
--   * IRR below 0.6 → Moderator training needed
++* TIGERScore drops below 75 → Alert team
++* Hallucination rate exceeds 7% → Pause auto-publishing
++* IRR below 0.6 → Moderator training needed
++
 . **Monthly Quality Reviews:**
--   * Analyze trends
--   * Identify systematic issues
--   * Plan prompt improvements
--   * Update AKEL models
-----
++* Analyze trends
++* Identify systematic issues
++* Plan prompt improvements
++* Update AKEL models
++-----
++
  ==== Metric Calculation Details ====
  **TIGERScore Implementation:**
++
  * Reference: https://github.com/TIGER-AI-Lab/TIGERScore
  * Input: Generated verdict + reference verdict (from expert)
  * Output: 0-100 score across 5 dimensions
@@ -1170,6 +1170,7 @@
  * Requires: Test set of expert-reviewed claims (minimum 100)
  **AlignScore Implementation:**
++
  * Reference: https://github.com/yuh-zha/AlignScore
  * Input: Generated verdict + source evidence text
  * Output: 0-1 faithfulness score
@@ -1176,11 +1176,12 @@
  * Calculation: Semantic alignment between claim and evidence
  **Source Quality Scoring:**
++
  * Use existing source reliability database (e.g., NewsGuard, MBFC)
  * Factor in: Publication history, corrections record, transparency
  * Scale: 0-1 (weighted average across sources)
-----
++-----
  ==== Integration Points ====
@@ -1211,11 +1211,13 @@
  == 14. Related Pages ==
  **Non-Functional Requirements (see Section 9):**
++
  * [[NFR11 — AKEL Quality Assurance Framework>>#NFR11]]
  * [[NFR12 — Security Controls>>#NFR12]]
  * [[NFR13 — Quality Metrics Transparency>>#NFR13]]
  **Other Requirements:**
++
  * [[User Needs>>FactHarbor.Specification.Requirements.User Needs.WebHome]]
  * [[V1.0 Requirements>>FactHarbor.Specification.Requirements.V10.]]
  * [[Gap Analysis>>FactHarbor.Specification.Requirements.GapAnalysis]]
@@ -1225,7 +1225,7 @@
  * [[Data Model>>FactHarbor.Specification.Data Model.WebHome]] - Data structures supporting requirements
  * [[Workflows>>FactHarbor.Specification.Workflows.WebHome]] - User interaction workflows
  * [[AKEL>>FactHarbor.Specification.AI Knowledge Extraction Layer (AKEL).WebHome]] - AI system fulfilling automation requirements
--* [[Global Rules>>FactHarbor.Organisation.How-We-Work-Together.GlobalRules.WebHome]]
++* [[Global Rules>>Archive.FactHarbor.Organisation.How-We-Work-Together.GlobalRules.WebHome]]
  * [[Privacy Policy>>FactHarbor.Organisation.How-We-Work-Together.Privacy-Policy]]
  = V0.9.70 Additional Requirements =
@@ -1285,6 +1285,7 @@
  **FactHarbor-Specific Mapping:**
  **Likelihood Score to Rating Scale:**
++
  * 80-100% likelihood → 5 (Highly Supported)
  * 60-79% likelihood → 4 (Supported)
  * 40-59% likelihood → 3 (Mixed/Uncertain)
@@ -1292,6 +1292,7 @@
  * 0-19% likelihood → 1 (Refuted)
  **Multiple Scenarios Handling:**
++
  * If claim has multiple scenarios with different verdicts, generate **separate ClaimReview** for each scenario
  * Add `disambiguatingDescription` field explaining scenario context
  * Example: "Scenario: If interpreted as referring to 2023 data..."
@@ -1324,7 +1324,6 @@
  * ✅ All required fields populated with correct data types
  * ✅ Handles multi-scenario claims correctly (separate ClaimReview per scenario)
--
  === FR45: User Corrections Notification System ===
  **Fulfills:** IFCN Principle 5 (Open & Honest Corrections), EFCSN compliance
@@ -1346,7 +1346,9 @@
  ==== Notification Mechanisms ====
--**1. In-Page Banner:**
++*
++**
++**1. In-Page Banner:
  Display prominent banner on claim page:
@@ -1366,10 +1366,10 @@
  * Public changelog at `/claims/{id}/corrections`
  * Displays for each correction:
--  * Date/time of correction
--  * What changed (before/after comparison)
--  * Why changed (reason if provided)
--  * Who made change (AKEL auto-update vs. contributor override)
++* Date/time of correction
++* What changed (before/after comparison)
++* Why changed (reason if provided)
++* Who made change (AKEL auto-update vs. contributor override)
  **3. Email Notifications (opt-in):**
@@ -1413,7 +1413,6 @@
  * ✅ Mobile-responsive banner design
  * ✅ Accessible (screen reader compatible)
--
  === FR46: Image Verification System ===
  **Fulfills:** UN-27 (Visual Claim Verification)
@@ -1431,23 +1431,25 @@
  **Purpose:** Find earlier uses of the image to verify context
  **Implementation:**
++
  * Integrate APIs:
--  * **Google Vision AI** (reverse search)
--  * **TinEye** (oldest known uses)
--  * **Bing Visual Search** (broad coverage)
++* **Google Vision AI** (reverse search)
++* **TinEye** (oldest known uses)
++* **Bing Visual Search** (broad coverage)
  **Process:**
++
 . Extract image from claim or user upload
 . Query multiple reverse search services
 . Analyze results for:
--   * Earliest known publication
--   * Original context (what was it really showing?)
--   * Publication timeline
--   * Geographic spread
++* Earliest known publication
++* Original context (what was it really showing?)
++* Publication timeline
++* Geographic spread
++
  **Output:**
--{{code}}
--Reverse Image Search Results:
++{{code}}Reverse Image Search Results:
  Earliest known use: 2019-03-15 (5 years before claim)
  Original context: "Photo from 2019 flooding in Mumbai"
@@ -1460,10 +1460,9 @@
  • 2020-07-22: Bangladesh monsoon
  • 2024-10-15: Current claim (misattributed)
--[View full timeline]
--{{/code}}
++[View full timeline]{{/code}}
-----
++-----
  **Method 2: AI Manipulation Detection**
@@ -1470,36 +1470,41 @@
  **Purpose:** Detect deepfakes, face swaps, and digital alterations
  **Implementation:**
++
  * Integrate detection services:
--  * **Sensity AI** (deepfake detection)
--  * **Reality Defender** (multimodal analysis)
--  * **AWS Rekognition** (face detection inconsistencies)
++* **Sensity AI** (deepfake detection)
++* **Reality Defender** (multimodal analysis)
++* **AWS Rekognition** (face detection inconsistencies)
  **Detection Categories:**
++
 . **Face Manipulation:**
--   * Deepfake face swaps
--   * Expression manipulation
--   * Identity replacement
++* Deepfake face swaps
++* Expression manipulation
++* Identity replacement
++
 . **Image Manipulation:**
--   * Copy-paste artifacts
--   * Clone stamp detection
--   * Content-aware fill detection
--   * JPEG compression inconsistencies
++* Copy-paste artifacts
++* Clone stamp detection
++* Content-aware fill detection
++* JPEG compression inconsistencies
++
 . **AI Generation:**
--   * Detect fully AI-generated images
--   * Identify generation artifacts
--   * Check for model signatures
++* Detect fully AI-generated images
++* Identify generation artifacts
++* Check for model signatures
++
  **Confidence Scoring:**
++
  * **HIGH (80-100%):** Strong evidence of manipulation
  * **MEDIUM (50-79%):** Suspicious artifacts detected
  * **LOW (0-49%):** Minor inconsistencies or inconclusive
  **Output:**
--{{code}}
--Manipulation Analysis:
++{{code}}Manipulation Analysis:
  Face Manipulation: LOW RISK (12%)
  Image Editing: MEDIUM RISK (64%)
@@ -1508,10 +1508,9 @@
  AI Generation: LOW RISK (8%)
--⚠️ Possible manipulation detected. Manual review recommended.
--{{/code}}
++⚠️ Possible manipulation detected. Manual review recommended.{{/code}}
-----
++-----
  **Method 3: Metadata Analysis (EXIF)**
@@ -1518,6 +1518,7 @@
  **Purpose:** Extract technical details that may reveal manipulation or misattribution
  **Extracted Data:**
++
  * **Camera/Device:** Make, model, software
  * **Timestamps:** Original date, modification dates
  * **Location:** GPS coordinates (if present)
@@ -1525,6 +1525,7 @@
  * **File Properties:** Resolution, compression, format conversions
  **Red Flags:**
++
  * Metadata completely stripped (suspicious)
  * Timestamp conflicts with claimed date
  * GPS location conflicts with claimed location
@@ -1532,8 +1532,7 @@
  * Creation date after modification date (impossible)
  **Output:**
--{{code}}
--Image Metadata:
++{{code}}Image Metadata:
  Camera: iPhone 14 Pro
  Original date: 2023-08-12 14:32:15
@@ -1545,19 +1545,20 @@
  Claim says: "Taken in Los Angeles"
  EXIF says: New York City
--⚠️ Edited 14 months after capture
--{{/code}}
++⚠️ Edited 14 months after capture{{/code}}
-----
++-----
  ==== Verification Workflow ====
  **Automatic Triggers:**
++
 . User submits claim with image
 . Article being analyzed contains images
 . Social media post includes photos
  **Process:**
++
 . Extract images from content
 . Run all 3 verification methods in parallel
 . Aggregate results into confidence score
@@ -1592,14 +1592,16 @@
  ==== Cost Considerations ====
  **API Costs (estimated per image):**
++
  * Google Vision AI: $0.001-0.003
  * TinEye: $0.02 (commercial API)
  * Sensity AI: $0.05-0.10
  * AWS Rekognition: $0.001-0.002
--**Total per image:** ~$0.07-0.15
++**Total per image:** $0.07-0.15**
  **Mitigation Strategies:**
++
  * Cache results for duplicate images
  * Use free tier quotas where available
  * Prioritize higher-value claims for deep analysis
@@ -1614,29 +1614,229 @@
  * ✅ Mobile-friendly image comparison interface
  * ✅ False positive rate <15%
--
  === FR47: Archive.org Integration ===
--Auto-save evidence sources to Wayback Machine.
++**Priority:** CRITICAL
++**Fulfills:** Evidence persistence, FR5 (Evidence linking)
++**Phase:** V1.0
--**Milestone:** Beta 0
++**Purpose:** Ensure evidence remains accessible even if original sources are deleted.
--=== FR48: Safety Framework for Contributors ===
++**Specification:**
--Protect contributors from harassment and legal threats.
++**Automatic Archiving:**
--**Milestone:** V1.1
++When AKEL links evidence:
--=== FR49: A/B Testing Framework ===
++1. Check if URL already archived (Wayback Machine API)
++2. If not, submit for archiving (Save Page Now API)
++3. Store both original URL and archive URL
++4. Display both to users
--Test AKEL approaches and UI designs systematically.
++**Archive Display:**
--**Milestone:** V1.0
++{{code}}
++Evidence Source: [Original URL]
++Archived: [Archive.org URL] (Captured: [date])
--=== FR50: OSINT Toolkit Integration ===
++[View Original] [View Archive]
++{{/code}}
++**Fallback Logic:**
++* If original URL unavailable → Auto-redirect to archive
++* If archive unavailable → Display warning
++* If both unavailable → Flag for manual review
++**API Integration:**
++
++* Use Wayback Machine Availability API
++* Use Save Page Now API (SPNv2)
++* Rate limiting: 15 requests/minute (Wayback limit)
++
++**Acceptance Criteria:**
++
++* ✅ All evidence URLs auto-archived
++* ✅ Archive links displayed to users
++* ✅ Fallback to archive if original unavailable
++* ✅ API rate limits respected
++* ✅ Archive status visible in evidence display
++
++== Category 4: Community Safety ==
++
++ FR48: Contributor Safety Framework ===
++
++**Priority:** CRITICAL
++**Fulfills:** UN-28 (Safe contribution environment)
++**Phase:** V1.0
++
++**Purpose:** Protect contributors from harassment, doxxing, and coordinated attacks.
++
++**Specification:**
++
++*
++**
++**1. Privacy Protection:
++
++* **Optional Pseudonymity:** Contributors can use pseudonyms
++* **Email Privacy:** Emails never displayed publicly
++* **Profile Privacy:** Contributors control what's public
++* **IP Logging:** Only for abuse prevention, not public
++
++**2. Harassment Prevention:**
++
++* **Automated Toxicity Detection:** Flag abusive comments
++* **Personal Information Detection:** Auto-block doxxing attempts
++* **Coordinated Attack Detection:** Identify brigading patterns
++* **Rapid Response:** Moderator alerts for harassment
++
++**3. Safety Features:**
++
++* **Block Users:** Contributors can block harassers
++* **Private Contributions:** Option to contribute anonymously
++* **Report Harassment:** One-click harassment reporting
++* **Safety Resources:** Links to support resources
++
++**4. Moderator Tools:**
++
++* **Quick Ban:** Immediately block abusers
++* **Pattern Detection:** Identify coordinated attacks
++* **Appeal Process:** Fair review of moderation actions
++* **Escalation:** Serious threats escalated to authorities
++
++**5. Trusted Contributor Protection:**
++
++* **Enhanced Privacy:** Additional protection for high-profile contributors
++* **Verification:** Optional identity verification (not public)
++* **Legal Support:** Resources for contributors facing legal threats
++
++**Acceptance Criteria:**
++
++* ✅ Pseudonyms supported
++* ✅ Toxicity detection active
++* ✅ Doxxing auto-blocked
++* ✅ Harassment reporting functional
++* ✅ Moderator tools implemented
++* ✅ Safety policy published
++
++== Category 5: Continuous Improvement ==
++
++ FR49: A/B Testing Framework ===
++
++**Priority:** CRITICAL
++**Fulfills:** Continuous system improvement
++**Phase:** V1.0
++
++**Purpose:** Test and measure improvements to AKEL prompts, algorithms, and workflows.
++
++**Specification:**
++
++**Test Capabilities:**
++
++1. **Prompt Variations:**
++
++* Test different claim extraction prompts
++* Test different verdict generation prompts
++* Measure: Accuracy, clarity, completeness
++
++2. **Algorithm Variations:**
++
++* Test different source scoring algorithms
++* Test different confidence calculations
++* Measure: Audit accuracy, user satisfaction
++
++3. **Workflow Variations:**
++
++* Test different quality gate thresholds
++* Test different risk tier assignments
++* Measure: Publication rate, quality scores
++
++**Implementation:**
++
++* **Traffic Split:** 50/50 or 90/10 splits
++* **Randomization:** Consistent per claim (not per user)
++* **Metrics Collection:** Automatic for all variants
++* **Statistical Significance:** Minimum sample size calculation
++* **Rollout:** Winner promoted to 100% traffic
++
++**A/B Test Workflow:**
++
++{{code}}
++1. Hypothesis: "New prompt improves claim extraction"
++2. Design test: Control vs. Variant
++3. Define metrics: Extraction accuracy, completeness
++4. Run test: 7-14 days, minimum 100 claims each
++5. Analyze results: Statistical significance?
++6. Decision: Deploy winner or iterate
++{{/code}}
++
++**Acceptance Criteria:**
++
++* ✅ A/B testing framework implemented
++* ✅ Can test prompt variations
++* ✅ Can test algorithm variations
++* ✅ Metrics automatically collected
++* ✅ Statistical significance calculated
++* ✅ Results inform system improvements
++
++=== FR54: Evidence Deduplication ===
++
++**Priority:** CRITICAL (POC2/Beta)
++**Fulfills:** Accurate evidence counting, quality metrics
++**Phase:** POC2, Beta 0, V1.0
++
++**Purpose:** Avoid counting the same source multiple times when it appears in different forms.
++
++**Specification:**
++
++**Deduplication Logic:**
++
++1. **URL Normalization:**
++
++* Remove tracking parameters (?utm_source=...)
++* Normalize http/https
++* Normalize www/non-www
++* Handle redirects
++
++2. **Content Similarity:**
++
++* If two sources have >90% text similarity → Same source
++* If one is subset of other → Same source
++* Use fuzzy matching for minor differences
++
++3. **Cross-Domain Syndication:**
++
++* Detect wire service content (AP, Reuters)
++* Mark as single source if syndicated
++* Count original publication only
++
++**Display:**
++
++{{code}}
++Evidence Sources (3 unique, 5 total):
++
++1. Original Article (NYTimes)
++   - Also appeared in: WashPost, Guardian (syndicated)
++
++2. Research Paper (Nature)
++
++3. Official Statement (WHO)
++{{/code}}
++
++**Acceptance Criteria:**
++
++* ✅ URL normalization works
++* ✅ Content similarity detected
++* ✅ Syndicated content identified
++* ✅ Unique vs. total counts accurate
++* ✅ Improves evidence quality metrics
++
++== Additional Requirements (Lower Priority) ==
++
++ FR50: OSINT Toolkit Integration ===
++
++
++
  **Fulfills:** Advanced media verification
  **Phase:** V1.1
@@ -1643,6 +1643,7 @@
  **Purpose:** Integrate open-source intelligence tools for advanced verification.
  **Tools to Integrate:**
++
  * InVID/WeVerify (video verification)
  * Bellingcat toolkit
  * Additional TBD based on V1.0 learnings
@@ -1657,6 +1657,7 @@
  **Purpose:** Verify video-based claims.
  **Specification:**
++
  * Keyframe extraction
  * Reverse video search
  * Deepfake detection (AI-powered)
@@ -1673,6 +1673,7 @@
  **Purpose:** Teach users to identify misinformation.
  **Specification:**
++
  * Interactive tutorials
  * Practice exercises
  * Detection quizzes
@@ -1688,12 +1688,12 @@
  **Purpose:** Share findings with IFCN/EFCSN members.
  **Specification:**
++
  * API for fact-checking organizations
  * Structured data exchange
  * Privacy controls
  * Attribution requirements
--
  == Summary ==
  **V1.0 Critical Requirements (Must Have):**
@@ -1716,7 +1716,6 @@
  * FR52: Detection Training
  * FR53: Cross-Org Sharing
--
  **Total:** 11 critical requirements for V1.0
  === FR54: Evidence Deduplication ===
@@ -1733,21 +1733,24 @@
  **Deduplication Logic:**
 . **URL Normalization:**
--   * Remove tracking parameters (?utm_source=...)
--   * Normalize http/https
--   * Normalize www/non-www
--   * Handle redirects
++* Remove tracking parameters (?utm_source=...)
++* Normalize http/https
++* Normalize www/non-www
++* Handle redirects
++
 . **Content Similarity:**
--   * If two sources have >90% text similarity → Same source
--   * If one is subset of other → Same source
--   * Use fuzzy matching for minor differences
++* If two sources have >90% text similarity → Same source
++* If one is subset of other → Same source
++* Use fuzzy matching for minor differences
++
 . **Cross-Domain Syndication:**
--   * Detect wire service content (AP, Reuters)
--   * Mark as single source if syndicated
--   * Count original publication only
++* Detect wire service content (AP, Reuters)
++* Mark as single source if syndicated
++* Count original publication only
++
  **Display:**
  {{code}}
@@ -1769,8 +1769,9 @@
  * ✅ Unique vs. total counts accurate
  * ✅ Improves evidence quality metrics
++== Additional Requirements (Lower Priority) ==
--== Additional Requirements (Lower Priority) ===== FR7: Automated Verdicts (Enhanced with Quality Gates) ===
++ FR7: Automated Verdicts (Enhanced with Quality Gates) ===
  **POC1+ Enhancement:**
@@ -1792,6 +1792,7 @@
  {{/code}}
  **Updated Verdict States:**
++
  * PUBLISHED
  * INSUFFICIENT_EVIDENCE
  * NON_FACTUAL_CLAIM
@@ -1813,4 +1813,3 @@
    Avg Source Quality: 0.73
    Quality Score: 8.5/10
  {{/code}}
--

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