{"id":"W2462204796","doi":"10.1016/j.ympev.2016.07.001","title":"PhyPA: Phylogenetic method with pairwise sequence alignment outperforms likelihood methods in phylogenetics involving highly diverged sequences","year":2016,"lang":"en","type":"article","venue":"Molecular Phylogenetics and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Phylogenetic tree; Pairwise comparison; Multiple sequence alignment; Biology; Sequence alignment; Phylogenetics; Sequence (biology); Tree (set theory); Alignment-free sequence analysis; Computational biology; Algorithm; Computer science; Genetics; Mathematics; Artificial intelligence; Gene; Combinatorics; Peptide sequence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008545621,0.0006420206,0.0005270218,0.0002136952,0.0002369538,0.00005866898,0.0004283972,0.0003282611,0.000006779529],"category_scores_gemma":[0.00009955111,0.0004737655,0.0001535955,0.0003507797,0.000404318,0.000005575894,0.0004547564,0.000153555,0.000005205106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001407306,"about_ca_system_score_gemma":0.0001973753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001208638,"about_ca_topic_score_gemma":0.000219488,"domain_scores_codex":[0.9962885,0.0004733894,0.000636752,0.001250832,0.0003939625,0.0009565819],"domain_scores_gemma":[0.9983822,0.00007010451,0.0002621423,0.0007704445,0.0002309692,0.000284126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001021597,0.00009477219,0.07309318,0.00004172896,0.0001648429,0.00001771257,0.0001235585,0.00119665,0.9053674,0.0004086861,0.00001817698,0.01937115],"study_design_scores_gemma":[0.002567568,0.001870059,0.1444625,0.000156318,0.0001824622,0.00008663955,0.0002769388,0.001136108,0.8404936,0.005569668,0.001893868,0.001304291],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8832973,0.01336628,0.1020738,0.0002817556,0.0001855536,0.0005751221,0.00006343197,0.00001384842,0.0001428948],"genre_scores_gemma":[0.8960797,0.003567381,0.09967692,0.0002187455,0.0001370298,0.0001437291,0.00002106131,0.00007966062,0.00007579554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07136928,"threshold_uncertainty_score":0.9997714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01401484049449976,"score_gpt":0.2758326871836884,"score_spread":0.2618178466891886,"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."}}