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Reference data are the key to implementing the STP architecture.
Unfortunately, costs of acquisition of such reference data are tremendous.
In this study, the actual measurement uncertainties of input and reference data are the main focus.
No reference data are available for surface complexation modelling at high alkaline pH.
Discrepancies between modelled and reference data are caused by different factors.
These reference data are used to teach the neural network how different parameters are correlated.
Retesting of adolescents with childhood-onset GH deficiency (GHD) is recommended, but age-related reference data are scarce.
No suitable reference data are available to assess the role of these control parameters or to test overall algorithm performance.
Matching accurate mass and retention time of each ligand with the reference data are required for confident structural assignment.
The reference data are composed of feature vector time-sequence data that are obtained from spoken keywords.
The unmatched reference data are calculated as false negative (FN). Figure 3 Matched extraction (a) and matched reference (b).
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