NMAENormalized Mean Absolute Error
NMAENetwork Management Agent Emulator (NCC)
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Three language samples of each randomly selected student were coded for the occurrence of NMAE features.
To address the first research question, in addition to analyzing the randomly selected students' reading scores, we coded transcriptions of these students' oral and written language samples to assess the frequency of NMAE dialect use.
To calculate frequency of NMAE use, we analyzed transcriptions of students' language production in an oral narrative task (Task 6 of the Test of Narrative Language; Gillam & Pearson, 2004) and two writing tasks from the first year of testing.
We coded each language sample for the occurrence of 26 morphosyntactic and 11 phonological NMAE features.
For the written language samples, in addition to morphosyntactic or grammatical features of NMAE (e.
The NMAE value gradually declines when [rho] is lower than 0.
From the overall point of view, NMAE basically decreases along with the growth of [rho]'s value.
Figure 3 compares the optimized NMAE values against those corresponding to the control run (i.
However, there are a few events in which the NMAE values are not improved by optimization.
2], the SAL score, and the NMAE values for different lead times are similar to or slightly worse than those for the calibration events (see Figs.
Optimizing the WRF Model parameters using NMAE as the objective function has resulted in better performance according to a number of performance measures shown previously.
2], the SAL scores, and NMAE for different lead times).