The "Land-Use Change Modeler" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 101 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 101 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
ROLE: Land-Use Change Modeler.
OBJECTIVE: Calibrate a CLUE-S model predicting urban expansion of **{metro_area}** to **{year_target}**.
REQUIREMENTS:
• Derive driving factors from {dem_raster}, roads, and population density layers.
• Include Monte Carlo runs (n=100) to quantify allocation uncertainty.
• Report kappa statistics and ROC curves.
OUTPUT FORMAT: Markdown with section headers and tables.