If only someone could figure out an algorithm to calculate posters' signal-to-noise ratio instead of post count...
I am a huge critic of the whole concept of Artificial Intelligence. But the problem you define actually is one that is more or less solved, albeit by harnessing human opinions.
I would propose a system similar to that used to rank reviews on Amazon. Forum members would have an optional binary vote on each post they read. It could be "helpful"/"not helpful", or "interesting"/"not interesting", depending on what the goals of signal-to-noise consideration would be. Call it "good"/"bad".
Forum members could then be ranked by total goodness, instead of total posts. Or more extremely they could be ranked by absolute goodness, which is total goodness minus total badness.
I believe that this would work and would be relatively easy to implement (compared to AI).
However, I think that DLJ's EEVblog time is better spent researching and filming blog entries rather than hacking forum software or screening real life people to do so. Therefore we should expect members to make their own decisions about the credibility of individual posters, based on standards similar to those that have been used on Usenet for decades.
After several weeks' reading the forums, or less if delving back into historical posts, any sensible user should get an impression about each of the regular posters, and will then know if a poster is reliable, or is frequently corrected, or simply has to respond to every message, whether s/he can add anything useful or not.