"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIRM_fitbacteria"	"fitbacteria"	"Bacteria Model Fitting"	1	"Start with the default settings. Run the model for 100 iterations for all 3 solvers (1-3). At the end of each fit, take a look at both the visual agreement between model and data, and the reported SSR. You'll probably want to have the plots with a y-axis on the log scale. Note that for this example, we have multiple measurements for each quantity for each time point. In our fitting approach, each such data point contributes to the SSR, so we are in some sense trying to fit average trajectories to the data. 
You should find that after the fitting, solver 2 produces the lowest SSR. However, the predicted model shows ups and downs in the bacteria variable that we don't really see in the data. While we'll find a better model later, it can sometimes happen that the model which fits best based on some statistical quantity, such as SSR, does not look good when doing e.g. visual checks. It's up to the modeler to decide what to conclude from that. My general suggestion is to try and further improve/tweak the model."	"T1R1"	"SSR after 100 interations for solvertype 2."	"Rounded_Numeric"	"Round to two significant digits"
"DSAIRM_fitbacteria"	"fitbacteria"	"Bacteria Model Fitting"	2	"Further tasks to come. For now, you are free to explore on your own."	"T2R1"	"Nothing"	"None"	""
