  Using bibliometric data artificially generated through a model of citation
dynamics calibrated on empirical data, we compare several indicators for the
scientific impact of individual researchers. The use of such a controlled setup
has the advantage of avoiding the biases present in real databases, and allows
us to assess which aspects of the model dynamics and which traits of individual
researchers a particular indicator actually reflects. We find that the simple
citation average performs well in capturing the intrinsic scientific ability of
researchers, whatever the length of their career. On the other hand, when
productivity complements ability in the evaluation process, the notorious $h$
and $g$ indices reveal their potential, yet their normalized variants do not
always yield a fair comparison between researchers at different career stages.
Notably, the use of logarithmic units for citation counts allows us to build
simple indicators with performance equal to that of $h$ and $g$. Our analysis
may provide useful hints for a proper use of bibliometric indicators.
Additionally, our framework can be extended by including other aspects of the
scientific production process and citation dynamics, with the potential to
become a standard tool for the assessment of impact metrics.
