Planetary Health Axis System (PHAS): An Active Learning Framework Integrating Data, Large Language Models, and Human Experts

This paper specifies the Planetary Health Axis System (PHAS), An active learning framework integrating data, large language models, and human experts for planetary health. The system is a pipeline of eight stages: (1) leakage-free preprocessing, mixture-of-experts imputation, and credibility-weighted data integration; (2) extraction of a dynamic, context-dependent knowledge prior from the scientific literature by a domain-tuned large language model; (3) nIDE non-invertible, asymmetric dimensionality expansion with median-kernel aggregation; (4) SCGL conditional structural graph learning on the lifted representation under a generalized-sparse, locally-dense, hub-mediated structure with acyclicity; (5) an active-learning, human-in-the-loop closure that converts learned structure into staged, verifiable queries; (6) non-discounted long-run-average optimal control subject to generalized planetary-boundary constraints, yielding KKT shadow prices and propagated top-level weights; (7) numerical solution by relative-value or policy iteration, with evaluation of the current state against the constrained optimum; and (8) full-system path/flux attribution together with unconditional and conditional distributional extrapolation. Together, the eight stages form a coupled estimation-and-control procedure that converts heterogeneous planetary-health evidence into verifiable structure and, ultimately, into boundary-respecting, decision-facing policies.