Scientific Paper · source-verified

A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power

Joynt, Veda; Cooper, Jacob; Bhargava, Naman; Vu, Katie; Kwon, O. Hwang; Allen, Todd R.; Verma, Aditi; Radaideh, Majdi I. · 2024-12-05

Generative AI tools, such as DALL-E and DreamStudio, show potential in improving public engagement and energy literacy about low-carbon sources like nuclear energy, though they fall short in representing technical details, addressing biases, and accurately depicting indigenous landscapes.

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Joynt, Veda; Cooper, Jacob; Bhargava, Naman; Vu, Katie; Kwon, O. Hwang; Allen, Todd R.; Verma, Aditi; Radaideh, Majdi I.. 2024-12-05. A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power. https://www.nature.com/articles/s41598-024-79705-4 (AI & Environment Resource Hub; record paper-052; collection snapshot 2026-09-15).

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Available source metadata

Author or creator
Joynt, Veda; Cooper, Jacob; Bhargava, Naman; Vu, Katie; Kwon, O. Hwang; Allen, Todd R.; Verma, Aditi; Radaideh, Majdi I.
Publisher
Scientific Reports
Publication date
2024-12-05
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day
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Unknown
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unknown
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unknown
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verified
Last verified
2026-09-15T16:44:27.954Z
Snapshot import
2026-09-06
Legacy domain
Governance, Justice & Society
Legacy subdomain
Using AI in Scientific Contexts with Nuclear Power Being An Example
journal
Scientific Reports

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Source sheet: Scientific Paper · Row 56 · Original ID: paper-052.

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A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.

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