Screen what comes in, and what went out
Assistant fingerprints, excess vocabulary, tortured phrases, DOIs that belong to another paper, cited works that were retracted, reused or AI-declared pictures — with the evidence, for submissions, for your recent output as the public record shows it, and for reviewer reports that look alike.
Use it as a policy, not a verdict
A strong signal is a reason for a person to look, never a rejection on its own: ask the author to explain the flagged passages, to supply original figures, to verify the flagged references. Write that into your ethics page: "submissions are screened for AI-writing, paper-mill and image signals; findings are put to the authors before any decision". The Journal Audit now checks for exactly that statement, for an AI-use policy and for an image-integrity policy.
Strong needs two independent families of signals; one family alone reads Notable at most, and the vocabulary is not counted when the authors declare language editing, translation, or English as an additional language (from 9 Oct 2026).
Shared duplicate check
Find out when a submission was screened by another journal taking part in the last 12 months. Opt in per journal; nothing textual is kept. Take part →
Screening at submission, by your own script
There is no ready-made OJS plugin for this yet. With the institution or partner API key, a script of your own (or your developer's, hooked to OJS's submission step) can post each new submission's text and get the same signals back as JSON: https://partners.smartscholars.in/api.php?action=screen. The API page shows the request and the answer.
What it cannot tell you
Whether a clean-looking text was written by a person — no tool can, and the ones that claim to misfire on technical English. Whether a figure that carries no metadata was generated. Whether a reviewer is real. It tells you what the text, the references and the files themselves show, with the rule and the source, so that your decision is yours and can be explained to the author.
