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Its default style reaches for structure even where none is needed. A one-line answer still arrives as a bulleted list. A single idea still gets a bolded lead-in like Key takeaway: this approach saves time. And a plain claim usually gets wrapped in a qualifier first, something like It is important to note that results may vary depending on the specific context. None of that is proof by itself, but stacked together across a whole draft, it reads machine-made.
The score reflects how machine-written the text reads overall, not which model produced it. Gemini's default style, heavy bolding, bulleted structure even in short answers, and frequent qualifiers, is the framing here, but the same check works on text from any model.
Build the ideas into sentences in your own structure and voice, then rescan. Reformatting the same flattened, over-qualified language into paragraphs rarely changes how it reads.
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See what gets flagged, then revise, rescan, and compare every version.
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