The LCA is a probabilistic approach to disease classification which allows the identification of more precise categories of disease conditions.
These concepts were further analysed to identify their similarities and differences, and they were grouped together to form more precise categories that were later organized into themes.
These concepts were further analysed to identify their similarities and differences; subsequently, they were grouped together to form more precise categories that were later organised into themes based on the research objective.
Second, LCM can be potentially improved through the use of concomitant variables, that is, variables that influence the prevalence of classes, thus permitting the identification of more precise categories.
In Experiment 1, we made an assumption that participants would use more precise trait categories to infer more exclusive groups, but this did not provide information on how large participants might expect these groups to be.
Consideration of more precise land-use categories and management processes such as irrigation, wood harvest treatment, biofuel crop type, and afforestation have been addressed for the upcoming 6th phase of Coupled Model Intercomparison Project (CMIP6).
Both Rcorrector and SEECER are significantly more precise (>86%% in all categories) and more sensitive than methods designed for DNA reads, especially for reads from low-expression transcripts.
Read codes have a hierarchical structure, with a top level code for a disease category branching into more precise and specific codes.
The scoring procedure is based on a previous study (Racette et al., 2006), with a more precise definition of the half-point category being applied in the present work.
Third, we grouped similar concepts together to form categories that were more precise, complete, and generalizable [ 25].
