Deductive bottlenecks also occur when there are hidden variables or stochastic factors that fundamentally limit the predictive capability of a purely inductive model. For example, knowing all the state variables of a chemical process would typically be expected to constitute sufficient information for predicting many properties. However, anomalous behavior might correlate with the shift of a particular technician. Knowledge of who is working a particular shift would constitute crucial missing information in such a case. Expanding variable space in such a manner is a remarkable but almost thoughtless process for the expert, but conventional ML has very little capacity for dealing with missing information. Hidden variables are also at play in the earlier example consisting of an unexpected reaction outcome due to a reagent impurity. In such a scenario, expert knowlxiii edge about expected reaction outcomes, the characteristic spectral signature of certain functional groups, and the number of species separated by chromatography may all play a role in deducing the out-of-distribution outcome.