Oracle's own FAQ concedes that reliably telling AI content from human content is impossible. So reducing volume depends on people honestly checking the box or more likely not submitting or reviewers spotting AI tells. Some of their example tells don't seem AI-specific, just bad code smells that reviewers probably rejected for prior to gen AI.
I'm reminded of academic peer review, which has run into a similar volume problem. Since there's more writing involved, there's also a lot of slop submitted and the reviews themselves can degenerate to slop, too. It's an unsolved problem. The things that seem to be making a difference are process changes like caps on submissions per author, immediate rejection prior to review, making authors review in return, etc.