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. Provide a weighted scorecard on a 1 to 5 scale with short justifications for each score. 7. End with a clear funding recommendation: `Strong Accept`, `Accept`, `Borderline`, or `Reject`. 8. Keep the review concrete and action-oriented. Reference proposal details when avai