Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.

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Liquid‐filled pores of Fischer‐Tropsch catalyst lead to slow diffusion of the reactants and can cause internal transport limitations leading to a significant decrease of selectivity and productivity. As an approach to overcome these limitations, transport pores can be added to provide an additional pathway for mass transport. In this work, a 3D isothermal model was developed, which takes the effect of concentration gradients within the transport pores into account. A comparison with a 1D model showed, that a description by a 3D model is necessary for transport pores with diameters larger than 10 µm.

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