Available Guardrails: Certifying Selective Prediction across ML Systems

A selective predictor can be statistically valid yet practically unavailable: with finite calibration data, it may fail to certify enough traffic at the reporting granularity required for deployment. We formalize certificate availability, the probability that a frozen candidate receives a valid certificate, and use exact binomial power as a planning quantity for certified service. The central empirical result is not that one partitioning algorithm dominates. The reliability order used by our contiguous dynamic program beats the median random order only $33\%$ of the time, while adding structured candidates to a diverse random candidate pool contributes only $0.0027$ mean coverage with median zero. Independent held-out comparison among diverse frozen candidates is more useful, but its gain over mean random-order balancing is still only $0.021$ with median zero. These findings turn the partition optimizer into a diagnostic of population headroom and redirect the deployable story toward two stronger levers. Under fixed semantic boundaries on HWU, direct constrained planning raises certified coverage at $J=34$ from $0.1742$ to $0.3523$ for DeBERTa and from $0.0861$ to $0.2601$ for DistilRoBERTa relative to support balancing. Separately, validity-preserving allocation of the familywise testing budget gains $0.011$ mean coverage from noisy planning data, increasing to $0.014$ with posterior-predictive availability. Population analysis confirms that substantially more certified traffic can exist in principle, while direct plug-in planning recovers only $0.005$, identifying finite estimation rather than optimization as the bottleneck. Across four additional domains, the same safety, granularity, and traffic constraint recurs, although no single planner dominates. The contribution is therefore a deployment formulation and empirical diagnosis of certified availability, together with practical mechanisms for recovering certified service under finite evidence.