Probabilistic identification matrices were developed by using data from 86 phenotypic clusters. In our analyses we used data gathered from 1,119 bacterial strains isolated from Alaskan outer continental shelf regions (320 features per isolate), the Jaccard similarity coefficient, and unweighted average linkage clustering. A normalized probability score (relative likelihood, identification score) based on a modification of the Bayes theorem and the ratio of observed likelihood to best possible relative likelihood were used as identification criteria. Error rates were compared for the proper association of isolates with 86 previously defined phenons by using an inclusive matrix of 61 feature probabilities and a hierarchical scheme containing a primary probability matrix (supermatrix) to indicate the most likely of six secondary probability matrices (submatrices) for detailed identification. The supermatrix-submatrix scheme was superior to the inclusive scheme on the basis of economy of tests and had a comparable error rate.


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