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The creation of computer models is often driven by the need to make predictions in regions where there is no data (i.e. extrapolations). This makes validation challenging as it is difficult to ensure that a model will be suitable when it is applied in a region where there are no observations of the system of interest. The current paper proposes a method that can reveal flaws in a model which may be difficult to identify using traditional approaches for model calibration and validation. Thedoi:10.20944/preprints201809.0389.v1 fatcat:b55pgvq4r5fzlleudhfylvvqse