ukubona × who india · θᵗ → L₀+Σwᵢ·Lᵢ → f(σ²,λ,ε) → γ|ε_FGT|² → L(θᵗ⁺¹)
AI Tools for Literature Review
Five levels. Five sessions each. A ROYGBIV loss gradient — from what we already believe to what we now know — built for WHO India health economists.
θᵗ → L₀+Σwᵢ·Lᵢ → f(σ²,λ,ε) → γ|ε_FGT|² → L(θᵗ⁺¹)
loss gradient · TMVES · tensor → matrix → vector → eigenmode → scalar
Red · A Priori · θᵗ
Orange · Bias & Weights · L₀+Σwᵢ·Lᵢ
Yellow · Vector · f(σ²,λ,ε)
Green · Eigenmode · γ|ε_FGT|²
Blue · Posteriori · L(θᵗ⁺¹)
five AI layers · evidence synthesis reference
I · Tensor · θᵗ
World AI
General science · ambient intelligence
The silent substrate — general scientific understanding across all domains already in the room
II · Matrix · L₀+Σwᵢ·Lᵢ
Perception AI
Ingestion · journals · grey sources
Ingests specific literature across journals, languages, and grey sources via PDF upload and API
III · Vector · f(σ²,λ,ε)
Agentic AI
Screening · extraction · repetitive tasks
Automates data extraction on identified literature — the highest-ROI layer for adoption right now
IV · Eigenmode · γ|ε_FGT|²
Generative AI
Synthesis · reports · policy briefs
Synthesizes findings into decision-ready documents — think Claude, not just ChatGPT
V · Scalar · L(θᵗ⁺¹)
Embodied AI
Human in loop · audit · conscience
Bias detection, equity checks, PRISMA compliance — the layer that makes the others trustworthy
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