Uncertainty and Calibration of Viability Prediction: From Estimation of V to the Probability of Future Viable Outcomes

This article formalizes the problem of uncertainty and calibration of viability prediction as the second metrological stage of Empirical Vitology after analysis of observability and identifiability of V. The following are distinguished: the current true, but not necessarily directly known, viability V(t); its estimate V̂(t); the viability profile Π_V(t); future viability V(t+τ); and the distribution of possible future outcomes. Instead of requiring a point prediction, a candidate probabilistic construction is introduced: 𝒫_V(t,τ) = P(V(t+τ) | 𝓘_t,O_V,M), where 𝓘_t represents all information legitimately available at the moment of prediction, O_V is the observation frame, and M is a prespecified model. If scalar V is not identifiable, the analogous construction is applied to Π_V, individual components, or a prespecified viability event. For a binary future event: p_V(t,τ) = P(E_V(t+τ)=1 | 𝓘_t), where E_V is defined before analysis of the outcome. The article distinguishes uncertainty of observations, reconstruction of V, parameters, model structure, process dynamics, metrological definition, distributional shift, and interlevel transfer. These are represented not as an automatically additive number but as an uncertainty profile: 𝕌_V = ⟨U_obs,U_rec,U_par,U_mod,U_proc,U_met,U_shift,U_level⟩. Probability calibration, prediction-interval coverage, probabilistic predictive accuracy, informativeness, and width of predictive distributions are formalized. It is emphasized that: a narrow uncalibrated prediction may be worse than a broad but correctly calibrated prediction. Model B must be compared with a strong domain-specific Model A not only by point error but also by prespecified probabilistic criteria, calibration, and independent predictive performance. Eight I2 hypotheses are formulated, including hypotheses of calibratability of V, horizon-dependent uncertainty, trajectory advantage, independent value of the uncertainty profile, and cross-domain transferability of probabilistic prediction architecture. I2 does not claim that a universally calibrated prediction of V already exists. The article defines the conditions under which viability prediction may move from a point assertion to a testable probabilistic scientific forecast. Keywords Vitology, viability, uncertainty, calibration, probabilistic prediction, predictive distribution, prediction interval, viability profile, viability trajectory, probability of viable outcome, forecasting, reproducibility, observability, identifiability, Model A, Model B, cross-domain metrology, Field of Life.