  We use the CMS Open Data to examine the performance of weakly-supervised
learning for tagging quark and gluon jets at the LHC. We target $Z$+jet and
dijet events as respective quark- and gluon-enriched mixtures and derive
samples both from data taken in 2011 at 7 TeV, and from Monte Carlo. CWoLa and
TopicFlow models are trained on real data and compared to fully-supervised
classifiers trained on simulation. In order to obtain estimates for the
discrimination power in real data, we consider three different estimates of the
quark/gluon mixture fractions in the data. Compared to when the models are
evaluated on simulation, we find reversed rankings for the fully- and
weakly-supervised approaches. Further, these rankings based on data are robust
to the estimate of the mixture fraction in the test set. Finally, we use
TopicFlow to smooth statistical fluctuations in the small testing set, and to
provide uncertainty on the performance in real data.
