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Additionally, relevant dose response matrices may be superimposed [11, 23, 25] to reveal different shapes of response surfaces, which may encode information of underlying biological network topology [11, 30, 31].
While our numerical study suggests building state-variable surrogates and using the relative error indicator for building log-likelihood surrogates, selecting appropriate type of surrogates and error indicators depends on the shapes of response surfaces.
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Shapes of responses were functions of home range sizes, individual detection probabilities, and spatial arrangement of animals and sampling stations.
Linear characteristics of the variables X1 and X2 implied the shape of response surfaces with maximum values located at the edges of the plot (Fig. 7a, b).
Design seismic forces depend on Peak Ground Acceleration (PGA) values and on the shape of Response Spectrum (RS) curves dictated by Building Codes or which need to be evaluated in every particular case.
While the response curves were similar among the three models, data prevalence markedly affected the shapes of the response curves.
The shapes of the response surfaces indicated the nature and extent of the interactions between the different factors [ 12, 13].
DCM for spontaneous fluctuations makes even less assumptions about the neural response and can capture different shapes of neural responses, e.g. mono- or biphasic.
The shapes of dose-response relations also differed among the studies.
A brief review of some models is given to illustrate the shapes of dose-response curve expected from them.
We review the experimental evidence for various shapes of dose-response relationships for carcinogens and summarize those experiments that give the most information on relatively low doses.
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