To build trust (I generally don't like the term, and prefer transparency) we need to be able to indicate if primary sources are still, umm, "valid". Some research turned out to be useful for some time, and still present interesting studies, but the conclusions are now false. Some research was based on fraudulent research and got retracted. Annotating primary sources in Wikidata with various forms of qualification is essential to moving science forward: ignoring mistakes and misconduct and not showing we know how to handle that, will contribute the a further blur of facts, fake, and fiction. If we cannot indicate a source was proven wrong, we will forget it, cite it again and again, and generally not learn from our mistakes.
Therefore, I think part of this proposal should be to make a start with developing models that adopt various community proposals that work in this area. I do not anticipate this to be solved in the first year, but doing something impactful over the period of the full project sounds quite achievable: data and tools are around, but integration and awareness is missing, two actions core to the proposal already. The first year (this annual plan) could work out a plan to integrate the the resources.
The first resources I like to see interoperable are those that provide information about retractions. These include the RetractionWatch database and PubMed (CrossRef may also have retraction information). Interoperability would start with the creation of suitable properties and a model that describes how retractions are showing up in Wikidata (probably with suitable ShEX). For the RetractionWatch database, maybe a property for their database entries may be sufficient. The more provenance about the retraction, the better, however.
The second source of information are citations. We already have a rich and growing citation network, but the current citations do not reflect the reason why the citations was made: this can include agreement, reuse of knowledge and data, but also disagreement, etc. The Citation Typing Ontology nicely captures various reasons why an article is cited. Some articles are cited a lot, but not because the paper turned our solid (e.g. http://science.sciencemag.org/content/332/6034/1163).
The importance is huge. The effort will be substantial, but the work can be largely done by the community, once the foundation and standards are laid out by Wikipedia/Wikidata. Repeatedly we find people citing retracted papers, citing papers with false information, and that alone is by scholars who read a substantial part of the research in their field. The impact will be substantial and use cases are easy to envision: use in policy development (which research should our governance based on, and which not), research funding (what is the long term quality of research at some institute (boring but solid, versus exciting by risky)), and doing research itself (is this paper still reflecting our best knowledge).
Of course, without this foundation we keep running into questions of reliability in Wikipedia too: can you automate alerting editors of articles where a cited paper is now considered false? Or regarding research, can false/true source ratio information be used to identify Wikipedia articles of dubious nature?
I fully understand that Wikimedia would go beyond state of the art of the research community, but the community is not doing itself. Just like it was not doing an Open, domain-independent resources, which turned out of great use in and to science. If our goal is the collection of all knowledge, this collection is not a mere pile of more and more knowledge, but must be bound by carefully judging the quality of that knowledge. For this, tracking the above types of information (retractions, citation types) is essential, IMHO.