ClimateAdapt Lab

ClimateAdapt Lab is an evidence-grounded decision-support for agricultural drought adaptation. A user picks a location, season and crop on a map. The platform computes drought-relevant indicators from CHIRPS v3 rainfall and ERA5-Land climate data (SPI, temperature and soil-moisture anomalies), retrieves cited passages from a curated scientific corpus (BM25 + multilingual-E5 hybrid retrieval), filters and ranks adaptation options with deterministic rules, and has Meta's Muse Spark draft a structured report. Every statement in that report is checked by deterministic code (tool-output ids, numbers, citations, no invented benefits or costs). Failing statements are replaced by platform-written text, and each statement is labelled model-written or platform-written. Nothing unvalidated reaches the user. The problem: agronomists, extension officers and planners must decide under uncertainty, but climate records, satellite archives and thousands of papers are scattered, and fluent AI summaries can hide invented numbers or citations. ClimateAdapt Lab makes numbers traceable and uncertainty visible: no severity score, explicit unknowns, full provenance. Built with FastAPI, React + Vite and Leaflet, with 900+ offline tests; judges can run it from the repo with zero keys in demo mode.