  The sixth assessment of the international panel on climate change (IPCC)
states that "cumulative net CO2 emissions over the last decade (2010-2019) are
about the same size as the 11 remaining carbon budget likely to limit warming
to 1.5C (medium confidence)." Such reports directly feed the public discourse,
but nuances such as the degree of belief and of confidence are often lost. In
this paper, we propose a formal account for allowing such degrees of belief and
the associated confidence to be used to label arguments in abstract
argumentation settings. Differently from other proposals in probabilistic
argumentation, we focus on the task of probabilistic inference over a chosen
query building upon Sato's distribution semantics which has been already shown
to encompass a variety of cases including the semantics of Bayesian networks.
Borrowing from the vast literature on such semantics, we examine how such tasks
can be dealt with in practice when considering uncertain probabilities, and
discuss the connections with existing proposals for probabilistic
argumentation.
