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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Atomic Canyon partners with INL on AI benchmarks
As interest and investment grows around AI applications in nuclear power plants, there remains a gap in standardized benchmarks that can quantitatively compare and measure the quality and reliability of new products.
Nuclear-tailored AI developer Atomic Canyon is moving to fill that gap by entering into a new strategic partnership with Idaho National Laboratory to develop and release the “first comprehensive benchmark suite for evaluating retrieval-augmented generation (RAG) and large language models (LLMs) in nuclear applications.”
Ming-Shih Lu, Theodor Teichmann
Nuclear Technology | Volume 102 | Number 2 | May 1993 | Pages 196-209
Technical Paper | Nuclear Fuel Cycle | doi.org/10.13182/NT93-A34817
Articles are hosted by Taylor and Francis Online.
The error characteristics of both high-resolution gamma spectroscopy and high-level neutron coincidence (HLNC) measurements, separate or combined, are discussed as they apply to nondestructive analysis of plutonium-bearing materials. Expressions have been derived to estimate the overall variance in the 240Pu effective mass and in the total plutonium mass in terms of the specific contributions from uncertainties in the measurements, the pertinent physical variables (including isotopics and impurities), and the instrumental constants. Experimental data available in the published literature have been used to illustrate the error characteristics of the HLNC and the effects of isotopic error correlations associated with materials having different burnups.