Supplementary Material — Extending Trainable Quantum Kernels for Aerodynamics Regression: AI-Assisted PRISMA-ScR Review Protocol and Data (SIINTEC 2026)

Supplementary material for the paper "Extending Trainable Quantum Kernels for Aerodynamics Regression: Classical Machine Learning Benchmarks and an AI-Assisted Systematic Review Case Study" (SIINTEC 2026, SENAI CIMATEC University). Design: adapted scoping review following PRISMA-ScR guidelines, with the PCC (Population, Concept, Context) eligibility framework. Database: Semantic Scholar corpus, accessed through the Elicit platform (Pro license). Search execution: July 2026. Funnel: 63 records identified → 7 excluded by automated screening → 3 reinstated by documented human audit → 59 included for structured extraction → 13 prioritized for deep reading. AI tools: Elicit (semantic search, screening, structured extraction), Research Rabbit (citation-network audit support), NotebookLM (deep-reading assistant and manuscript structuring). All AI outputs were audited by the human authors, who assume full responsibility for the final content.