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原文摘要

Many benchmarks for rare events score a prediction in three steps: pool the prediction and the target over spatial blocks, threshold each pooled field, and score the resulting contingency table. We show that at a fixed rare threshold the max-pooled Critical Success Index (CSI) cannot separate two different things: how well a system places events, and how well its amplitudes are calibrated. Sharp observations push many blocks above the threshold, while the attenuated predictions of squared-error regression leave those same blocks below it. A monotone calibration map is fitted on held-out data and applied separately to each system. The map needs no retraining and cannot change the order of any two pixels, so whatever part of a difference it reproduces cannot be evidence of better spatial ranking. We report how much of a reported difference the map removes. On SEVIR, two released checkpoints of one architecture differ by -29.5% in extreme-threshold CSI before the control and by +5.3% after it. Across 450 pairwise contrasts among 6 systems, how far apart two systems sit in pooled frequency bias predicts how far their CSI difference moves under the control (r = 0.896 when the map is fitted after pooling), and 64 contrasts reverse sign. At CasCast's published extreme-event operating point, calibration alone removes 88%-106% of the cascade's gain over its backbone across three controls. Calibration also reveals advantages hidden by a better-calibrated baseline. On geostationary infrared imagery, a larger share of the score can be explained by calibration as events get rarer. The problem therefore needs two things at the same time: a threshold that is fixed in advance, and a model whose output is poorly calibrated at that threshold. We recommend reporting pooled frequency bias and a symmetric held-out FreeKnob Audit alongside rare-event pool-and-threshold scores.