SPARC is a Tauri/React desktop app wrapped around one thing: a 7-stage neural pipeline with built-in causal inference. Build a project, run the pipeline, simulate adaptation — every stage is reproducible, every prediction has a posterior.
The pipeline is the product. Each stage gates the next. χ², σ, and physics constraints are checked before you can advance. Stages are reproducible from project.yml — bring the same file to a different machine and get bit-identical posteriors.
At the heart of SPARC: a locally-weighted neural ensemble trained under directed causal constraints. Edges are propose-then-adjudicate; signs are physically grounded; coefficients vary smoothly across space. The result is a model that doesn't just fit — it admits to a theory of how the world works.
An adaptation scenario is a perturbation applied to the trained ensemble. Increase canopy. Lower impervious. Raise sea level by 0.6m. SPARC re-runs prediction across the grid and returns a posterior mean, σ, and a pixel-wise uncertainty raster — never a single number.
Every coefficient is interrogable. Every edge has a sign. Every prediction has a posterior.
Monotone constraints, mass balance, conservation laws — the model honors them or it doesn't run.
project.yml is the source of truth. Every result is regenerable from a single command, 5 years from now.