7. A machine implemented method, comprising: obtaining a plurality of documents from at least one of a storage device or a network; receiving a judgment task utilized by an assessor for a comparative evaluation of the plurality of documents; generating a selection panel that includes at least one document from the plurality of documents for evaluation by the assessor, the generating including determining whether to present one or more of a plurality of interfaces to the assessor based on the judgment task, the plurality of interfaces including an absolute judgment panel, a binary selection panel, and a graded preference panel, wherein the absolute judgment panel presents a single document for the assessor to evaluate, the absolute judgment panel requests the assessor to select at least one ranking criteria associated with the single document, the binary selection panel presents at least two documents from the plurality of documents for review by the assessor, the assessor selects one of the at least two documents as being binarily preferable over another of the at least two documents, and the graded preference panel presents at least two documents from the plurality of documents for review by the assessor, the assessor identifies one of the at least two documents as being preferable, the one of the at least two documents identified as preferable being retained as one of the at least two documents for a next judgment; presenting the selection panel to the assessor and receiving a response from the assessor, the response based on a comparison of documents comprising the plurality of documents with each other; comparatively ranking the response from the assessor with a previously elicited response from the assessor; and producing a ranked list of documents based at least in part on the ranking of the response from the assessor.
13. The method of claim 7 , one or more of the generating, the presenting, the receiving, the ranking, or the producing further employing a dynamically trainable probabilistic model.