Slop wastes editorial time
Editorial teams, often volunteers, perform vital work in advancing human knowledge. A submission that takes little effort to produce can still take an editor hours to read and assess.
Our approach
Editorial teams need time to read, assess and advance worthwhile work. We focus on low-effort submissions that consume that time, whatever tools were used to produce them.
Three truths
The pressure on review comes from the time and attention low-effort work demands. AI is part of the picture, but it is not the whole story.
Editorial teams, often volunteers, perform vital work in advancing human knowledge. A submission that takes little effort to produce can still take an editor hours to read and assess.
One detailed study at Organization Science reported a 42% rise in submissions since ChatGPT launched, with AI use accounting for nearly all of the increase. It also found detectable AI use in over 30% of that journal’s peer reviews. Read the journal’s report
AI can help accelerate research and make participation more accessible to people facing barriers in academia. People produce low-effort work with or without AI; AI can make it faster and more convincing. That is the problem we address.
Why we don’t use AI detection

Text that has been through a large language model can still be valuable. And text with no AI watermark can still be low-effort, low-quality and a poor use of scarce editorial time.
AI detectors try to infer how text was produced. That is not the same as assessing the work itself. We believe the challenge facing reviewers is not simply AI; it is slop in general.
Why we built Alluvial
Alluvial looks for specific quality signals and gives editors findings they can examine. It helps bring relevant evidence into view; it does not determine which papers deserve editorial time or decide whether to reject them. Those decisions remain with editors.
