The connection between monophyly and efficient taxonomic diagnoses is elaborated. The inefficiency of nonmonophyletic groups is shown by reconstructing data matrices from hierarchical sets of diagnoses that are derived from apomorphies and read in order from highest to lowest rank. The practice of diagnosing nonmonophyletic groups either results in omitting data, resulting in errors in reconstructed datasets, or repeating character information to make up for the implied losses. Step‐by‐step demonstrations with hypothetical and real data are used as guidance. Provisions are made for missing, inapplicable and polymorphic data. Slow optimization (delayed transformation) is useful for choosing a state reconstruction in order to report apomorphies completely. The diagnoses of paraphyletic groups can be expressed in different ways, including regrafting derived clades, reanalyzing data with constraints, and reading the original diagnoses in a different order––the last is the least efficient. A cladistic version of the data compression ratio is proposed to quantify the diagnostic efficiency of a cladogram.

中文翻译：

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单系分类和信息内容

详细阐述了单系和有效分类诊断之间的联系。非单系群的低效率通过从从无形性派生的诊断的分层集合中重建数据矩阵来显示，并按从最高到最低等级的顺序读取。诊断非单系群的做法要么导致省略数据，导致重建数据集出现错误，要么重复字符信息以弥补隐含的损失。使用假设和真实数据的逐步演示作为指导。对缺失、不适用和多态的数据做出了规定。慢速优化（延迟转换）对于选择状态重建以完全报告变形非常有用。并系群的诊断可以用不同的方式表达，包括重新移植派生的进化枝，重新分析带有约束的数据，以及以不同的顺序读取原始诊断——最后一个是效率最低的。提出了数据压缩比的分支版本来量化分支图的诊断效率。