{'fraction_non_alphanumeric': 0.013541666666666667, 'fraction_numerical': 0.0, 'mean_word_length': 5.322368421052632, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
DERIVING HIGH QUALITY FULLY RESOLVED CONSENSUS TREES

The consensus tree problem primarily arises in the domain of phylogenetics and seeks to find for a given collection of trees a single tree best representing it. Such a tree collection is usually obtained by biologists for a specific taxa set either via different phylogenetic inference methods or multiple applications of a nondeterministic procedure. Existing consensus methods often have the disadvantage of being very strict, limiting the resulting consensus tree in terms of its resolution and/or precision. A reason for this typically is the coarse granularity of the tree metric used. Hence we both utilize the fine-grained TreeRank similarity measure within metaheuristics and apply exact methods based on appropriate integer linear programming (ILP) models to find fully resolved (binary) consensus trees of high quality. We further give results on several real and new artificially generated data.