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] Researchers conducted a pre-registered audit evaluating large language models (LLMs) as essay graders using public corpora from the ENEM and ASAP datasets. ^[2] Testing on 12 US wildfires demonstrated that the correlation with ground survey results was 0.72 when using images from different seasons. ^[3] The 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] The system works by analyzing seasonal changes in unaffected areas and applying them to pre-fire imagery to isolate actual fire damage. ^[5] All five version contrasts showed severity shifts beyond a family-wise permutation null, with one shift reaching up to 133 points. ^[6] Correlations between LLM judges and human raters ranged from 0.47 to 0.56, indicating undiscriminating performance. ^[7] The study analyzed 2,377 essays graded by 12 judges from four different providers across five version contrasts. ^[8]