The phrase "a model term" is correct and usable in written English.
It can be used when referring to a specific term that serves as a standard or example in a particular context, such as in academic or technical writing.
Example: "In our research, we identified 'sustainability' as a model term for environmental studies."
Alternatives: "a standard term" or "an exemplary term".
According to the behavior of the samples fitting in the equations, the existence of crystalline phase, amorphous phase, and dislocation of elastic dipole is visible, and finally, the H - A model term characterizes the presence of defects in materials by attention to Brown's theory.
In another study performed in the same cohort, a relation was found between diabetes and infarcts [47]; however, we were also unable to mediate the PCK1 association by including a model term for cerebral infarctions.
Indeed, after inclusion of a model term for global AD pathology, the PCK1 SNP (rs8192708) remained associated with global cognition (Beta = 0.37, p = 0.002), despite the strong, independent association between pathology and cognition (Beta = −1.22, p<2×10−16; Table 7).
Given our finding of an association with neuritic plaques, we substituted a model term for neuritic pathology instead of the global pathology variable; however, despite a modest reduction in the effect size, the relation between PCK1 and cognitive impairment remained significant (Beta = 0.31, p = 0.005; Table 7).
"Reference level" gives factor value against which a model term was cross-checked.
A step-down hierarchical procedure was used in order to eliminate noncontributory terms in the models being explored, with criterion for inclusion of a model term being P < 0.1.
We designed a model, termed hRMN that integrates different genomic features and implemented a variant of belief propagation for functional annotation transfer.
While this model was significant (P<0.0001), only seed weight was significant as an individual model term (P<0.0001) while SLA and H were not (P = 0.261 and 0.190, respectively).
These data sets are joined in a probabilistic model, termed a dynamic hierarchical stochastic block model, to infer network evolution.
Thus, a prediction model, termed Poly(A) site classifier, or PAC, was constructed.
The regression analysis of the experimental design showed that the linear model terms (A, B, and C) and the quadratic model term (A) were significant.
