{"id":"W2008918578","doi":"10.1371/journal.pone.0014373","title":"Sequence Alignment, Mutual Information, and Dissimilarity Measures for Constructing Phylogenies","year":2011,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Mutual information; Metric (unit); A priori and a posteriori; Heuristic; Context (archaeology); Sequence (biology); Similarity (geometry); Algorithm; Information theory; Simple (philosophy); Measure (data warehouse); Computer science; Multiple sequence alignment; Mathematics; Sequence alignment; Data mining; Artificial intelligence; Statistics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004858208,0.001446962,0.000882627,0.006688677,0.0008939906,0.001959598,0.001734425,0.001630604,0.001918991],"category_scores_gemma":[0.02368026,0.000490353,0.001124156,0.006866466,0.002045896,0.004259946,0.002193221,0.002477478,0.0008107917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001571335,"about_ca_system_score_gemma":0.0008201982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006033918,"about_ca_topic_score_gemma":0.000668323,"domain_scores_codex":[0.995076,0.002075603,0.0003347851,0.0007389922,0.001665349,0.0001093365],"domain_scores_gemma":[0.9848364,0.01066028,0.001742007,0.001413292,0.001040701,0.0003073233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003513491,0.000202662,0.01595068,0.001550808,0.0006404993,0.0003142925,0.0006401257,0.2190221,0.01286233,0.2700579,0.005880067,0.4725272],"study_design_scores_gemma":[0.00003450658,0.0002437671,0.008233043,0.0002178978,0.0001008848,0.0007554158,0.00017562,0.4896589,0.007585286,0.4823922,0.01043615,0.0001663685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02422466,0.003541761,0.967868,0.0004261003,0.00007360538,0.0001224028,0.0005873885,0.0004450388,0.002711029],"genre_scores_gemma":[0.2868921,0.001982811,0.7072526,0.0001657476,0.0003038711,0.0006078908,0.001543052,0.0003069094,0.000944919],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006688677,"threshold_uncertainty_score":0.025693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07828900148832069,"score_gpt":0.23542067232773,"score_spread":0.1571316708394093,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}