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Science has always started with people with a question, an idea, and the work to turn it into a finding. Today, tools like Claude for Science help researchers do that faster than ever. But as the number of findings grows, so does an urgent question: which ones can we actually trust? A result can reproduce perfectly and still be wrong. A model may have learned the patient instead of the disease, or the scanner instead of the biology. Acting on those artifacts costs researchers time, funding, and— ultimately time patients do not have. So we built Crucible. Crucible takes an analysis—its code, data, and claim—reproduces the original result, and then puts the finding itself on trial. Four Claude reviewers challenge it from different directions. Crucible re-executes their tests and produces a Report Card backed by reproducible exhibits, exact rerun commands, and concrete fixes. Researchers can bring that evidence into Claude for Science, repair the analysis, and ask Crucible to verify it again. Crucible is the safeguard for the AI wave in science.

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