  Model defects are known to cause biased nuclear data evaluations if they are
not taken into account in the evaluation procedure. We suggest a method to
construct prior distributions for model defects for reaction models using
neighboring isotopes of $^{56}$Fe as an example. A model defect is usually a
function of energy and describes the difference between the model prediction
and the truth. Of course, neither the truth nor the model defect are
accessible. A Gaussian process (GP) enables to define a probability
distribution on possible shapes of a model defect by referring to intuitively
understandable concepts such as smoothness and the expected magnitude of the
defect. Standard specifications of GPs impose a typical length-scale and
amplitude valid for the whole energy range, which is often not justified, e.g.,
when the model covers both the resonance and statistical range. In this
contribution, we show how a GP with energy-dependent length-scales and
amplitudes can be constructed from available experimental data. The proposed
construction is inspired by a technique called dynamic time warping used, e.g.,
for speech recognition. We demonstrate the feasibility of the data-driven
determination of model defects by inferring a model defect of the nuclear
models code TALYS for (n,p) reactions of isotopes with charge number between 20
and 30. The newly introduced GP parametrization besides its potential to
improve evaluations for reactor relevant isotopes, such as $^{56}$Fe, may also
help to better understand the performance of nuclear models in the future.
