Predictive Structural Intelligence in Practice: Calibration Cases Across AI, Institutions, Care, and Infrastructure

This paper develops the case-calibration layer of Predictive Structural Intelligence. Earlier predictive SI papers proposed breach hazard as a way to ask whether contradiction is being metabolized into binding repair quickly enough to prevent structural debt, buffer exhaustion, hysteresis, and burden export from forcing contact in a harder form. This paper asks what happens when those instruments meet concrete cases. It applies the predictive runtime to known SI case-types: clinical decision support, high-risk AI compliance, public administration and risk scoring, unsupported AI output, AI companionship and synthetic witness, logistics and supply-chain optimization, high-drift corporate failures, and psychological or coaching material. Each case is read through the same calibration grid: observed structure, initial state, anchor variables, hidden holder, predicted trajectory, output state, falsifier, repair condition, and overread risk. The paper does not claim statistical validation or exact collapse timing. Its contribution is methodological: it shows where Predictive SI strengthens judgment, where it must downgrade, and where evidence is too thin for responsible prediction. The central result is that hidden-holder dependence, contradiction recovery lag, synthetic trace, and burden export appear as transferable warning structures, while human and care contexts require special restraint.