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A probabilistic model is described that helps explain how score-based algorithm fusion achieves significant false alarm reduction. The two-class detection and classification (DC) problem is considered: the target and the nontarget. Multiple DC algorithms, based on fundamentally different DC methodologies, process the same sensor data looking for target-like objects. Each object detected and classified as target-like by a given algorithm is assigned a positive score, which indicates the degree to which the algorithm considers the object target-like. Score-based algorithm fusion is the fusion of multiple detection & classification algorithms where only the scores of the individual algorithms are used to make a final determination on whether an object is a target or not Despite the fact that only the scores are used in the fusion process, false alarm reduction has been remarkable while still preserving a high probability of target detection and classification. A probabilistic model is presented in this paper that supports this observation.