During the past two decades the ecosystem of biomedical publications has moved from a print-based to a mainly Web-based model. However, this transition brings with it many new problems, in the context of an exponentially increasing, intractable volume of publications [1,2]; of systemic problems relating to valid (or invalid) citation of scientific evidence [3,4]; rising levels of article retractions [5,6] and scientific misconduct [7]; of uncertain reproducibility and re-usability of results in therapeutic development [8], and lack of transparency in research publication [9]. While we now have rapid access to much of the world’s biomedical literature, our methods to organize, verify, assess, combine and absorb this information in a comprehensive way, and to move discussion and annotation activities through the ecosystem efficiently, remain disappointing. [...] Computational methods previously proposed as solutions include ontologies [10]; text mining [2,11,12]; databases [13]; knowledgebases [14]; visualization [15]; new forms of publishing [16]; digitial abstracting [1]; semantic annotating [17]; and combinations of these approaches. However, we lack a comprehensive means to orchestrate these methods. We propose to accomplish this with a layered metadata model of scientific argumentation and evidence.