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    "prose": "Researchers at the University of Science and Technology (UNIST) in South Korea developed a satellite analysis technique called ASAP to accurately identify wildfire damage regardless of seasonal changes. [^1]\n\nResearchers conducted a pre-registered audit evaluating large language models (LLMs) as essay graders using public corpora from the ENEM and ASAP datasets. [^2]\n\nTesting on 12 US wildfires demonstrated that the correlation with ground survey results was 0.72 when using images from different seasons. [^3]\n\nThe ASAP technique filters out natural vegetation changes, such as autumn foliage, which often lead to errors when comparing satellite images taken in different seasons. [^4]\n\nThe system works by analyzing seasonal changes in unaffected areas and applying them to pre-fire imagery to isolate actual fire damage. [^5]\n\nAll five version contrasts showed severity shifts beyond a family-wise permutation null, with one shift reaching up to 133 points. [^6]\n\nCorrelations between LLM judges and human raters ranged from 0.47 to 0.56, indicating undiscriminating performance. [^7]\n\nThe study analyzed 2,377 essays graded by 12 judges from four different providers across five version contrasts. [^8]",
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