proposal-reviewlisted

# Proposal Review Produce a rigorous, decision-ready review for AI/ML, computational biology, and bioscience proposals. Be fair, skeptical, specific, and explicit about missing information. ## Instructions 1. Read the proposal and identify the decision context if provided: sponsor goals, rubric, budget cap, timeline, and risk tolerance. 2. If critical information is missing, do not invent it. Flag the gap and turn it into a prioritized question for the PI. 3. Structure the review with these sections: - Executive summary - Heilmeier catechism - Technical merit - Data, compute, and experimental resources - Risk register - Team and execution capability - Ethics, safety, and compliance - Budget and schedule realism - Scorecard - Decision and funding conditions - Questions for the PI 4. Tailor the technical review to the proposal type: - AI/ML: baselines, ablations, leakage prevention, calibration, external validation, compute realism - Bio or wet lab: controls, replicates, statistical plan, assay feasibility, translational path 5. Include at least six risks covering technical, data or experimental, budget or timeline, and adoption or regulatory concerns when relevant. 6. If the sponsor supplies a rubric, use its categories, weights, and decision vocabulary. Otherwise use the default 1-to-5 scorecard below; do not mix sponsor and default weights. 7. Default weights: strategic fit and novelty 15%, technical rigor 25%, feasibility and resour