While this high performance makes spec2struct a powerful tool, it can only find practical utility if it can also indicate whether a prediction is correct or wrong. For this reason, we analyzed whether the empirical error rate is correlated with the distance within the latent space (which can be thought of as a so-called conformity score). In our case, we analyze how the multimodal similarity score is related to the accuracy shown in Figure . The resulting calibration curve is shown in Figure , where we observe a linear trend between the scaled scores and the proportion of correctly predicted structures, demonstrating a well-calibrated scoring function, implying that the cosine similarities can be used as reliable confidence estimate. Identifying incorrect structure assignments According to Figure , if the score is above a scaled threshold of 0.918, there is a probability of around 98 % that the prediction made by the genetic algorithm is correct. This observation allows us to convert spec2struct into a practical error detection tool that can be applied upon the entry of spectra in databases or directly in an electronic lab notebook. For example, to detect if a spectrum has, by mistake, been attached to the wrong compound. In those incorrect assignments, we would expect a low similarity score and also that our GA can propose a better matching compound.