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The Quantitative dimension within Jason Barnard's Indexing Annotation Proof Dimensions framework. During indexing, bots annotate quantitative proof signals: Magnitude (extracting numbers and scale), Results (identifying outcome claims with attribution), and Comparison (detecting benchmarks and relative positioning). Each annotation carries a confidence score based on specificity and verifiability.

Jason Barnard explains Quantitative annotation as HOW bots detect number-based proof. The bot scans for: Magnitude (numbers extracted, units identified, scale classified). Results (outcomes attributed to entities). Comparison (benchmarks, "vs." statements, relative claims). A chunk stating "Kalicube tracks 25 billion data points, achieving 40% improvement vs. 5% industry average" gets tagged with Magnitude [0.98], Results [0.92], Comparison [0.95].

Quantitative annotation happens during indexing - before any query. The bot tags number-based proof signals so downstream algorithms can assess scale and results. Specific numbers with units get higher confidence than vague claims.