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Parameterization: In a model, quantity CKGETMPi A CKGETMPi is modelled. CKGETMPi A CKGETMPi is influenced by unknown CKGETMPi B CKGETMPi . Parameterization is to express CKGETMPi B CKGETMPi using CKGETMPi A CKGETMPi and/or other known quantities , such that CKGETMPi A CKGETMPi can be estimated . Parameterization scheme s usually consist of parameters to be determined empirically. Example, we model population density CKGETMPi ρ CKGETMPi using model ( conservation equation ) d CKGETMPi ρ CKGETMPi /d CKGETMPi t CKGETMPi = CKGETMPi s CKGETMPi , where CKGETMPi s CKGETMPi is birth-death rate , but unknown. Because of this, the model is not “closed”. Since CKGETMPi s CKGETMPi is too difficult to estimate , we express CKGETMPi s CKGETMPi using CKGETMPi ρ CKGETMPi , e.g., s CKGETMPi = r CKGETMPi ρ CKGETMPi , where r CKGETMPi is a parameter. The model is now d CKGETMPi ρ CKGETMPi /d CKGETMPi t CKGETMPi = CKGETMPi r CKGETMPi CKGETMPi ρ CKGETMPi which is the Mathus (1798) population growth theory. We use p arameterization to describe our understanding of the processes. It is a vital technique for representing cross-scale and cross-compartment interactions in complex systems . ( YS, 12.06.2024 ).