Evidence Scoring

Context Block

Page Type: Evidence System Layer
Function: Quantitative Evaluation Engine
Position: After Evidence Classification
Role: Converts categorized evidence into measurable confidence scores

This layer translates qualitative classification into numerical signals that can be used for ranking, filtering, and decision-making in downstream systems.

Core Objective

• Quantify evidence quality into standardized scores
• Measure reliability across multiple dimensions
• Enable ranking and prioritization of evidence
• Support conflict resolution and filtering
• Provide confidence inputs for answer generation

Scoring Pipeline

1. Authority Evaluation
Assesses credibility of the source producing the evidence.

2. Relevance Scoring
Measures alignment between evidence and query intent.

3. Freshness Analysis
Evaluates temporal validity and update recency.

4. Verifiability Check
Determines whether evidence can be independently confirmed.

5. Composite Score Generation
Combines all metrics into final confidence index.

Scoring Dimensions

• Authority Score — source trustworthiness level
• Relevance Score — query alignment strength
• Freshness Score — temporal validity
• Verifiability Score — traceability and confirmability

Final Output: Evidence Confidence Index (0–1)

Example Scoring

Evidence: Official Google Search documentation

• Authority: 0.95
• Relevance: 0.90
• Freshness: 0.85
• Verifiability: 0.98

Final Score: 0.92 → High-confidence evidence

Score Interpretation

• 0.80 – 1.00 → High-confidence evidence (primary usage)
• 0.50 – 0.79 → Medium-confidence evidence (supporting usage)
• 0.00 – 0.49 → Low-confidence evidence (filtered or downgraded)

Integration in GEO Pipeline

• Evidence Classification
• Evidence Ranking
• Evidence Validation
• Evidence Confidence Model

Evidence Scoring acts as the quantitative backbone of the Evidence system, enabling objective comparison between heterogeneous information sources.

Failure Modes

• Overweighting authority while ignoring relevance
• Freshness bias overriding stable authoritative sources
• Incorrect verifiability estimation
• Score inflation due to redundant evidence signals

Structured Output Model

Each evidence unit produces:

• Authority Score
• Relevance Score
• Freshness Score
• Verifiability Score
• Composite Evidence Confidence Index

Relationship Block

Parent Layer: /evidence/
Upstream: Evidence Classification
Downstream: Evidence Ranking, Evidence Validation
Connected Systems: Retrieval Engine, Ontology Layer, Answer System

Structured Summary

Evidence Scoring is the quantitative evaluation layer of the Evidence system. It converts classified evidence into measurable confidence signals that determine reliability and usage priority.

This layer ensures that only high-quality, verifiable, and relevant evidence influences downstream reasoning and answer generation.