{"id":"W2792658054","doi":"10.7717/peerj.4873","title":"MIPhy: identify and quantify rapidly evolving members of large gene families","year":2018,"lang":"en","type":"article","venue":"PeerJ","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Phylogenetic tree; Gene duplication; Biology; Phylogenetics; Evolutionary biology; Gene family; Gene; Lineage (genetic); Genetics; Organism; Genome; Computational 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001895481,0.00008364383,0.0001081627,0.00003907929,0.00004536089,0.00001053016,0.00006529926,0.00007318505,0.00002918187],"category_scores_gemma":[0.00008916621,0.00007881207,0.00003890997,0.0000642927,0.00007309631,0.000003327312,0.00005527256,0.00003640601,0.00000532047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002460137,"about_ca_system_score_gemma":0.00001122698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000060517,"about_ca_topic_score_gemma":0.00003417757,"domain_scores_codex":[0.9994549,0.00001557961,0.0001231572,0.0002055419,0.00006316246,0.0001377047],"domain_scores_gemma":[0.9996342,0.000001751135,0.00004194002,0.0001891697,0.0001012626,0.00003168696],"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":[0.00001401416,0.00001550673,0.00107241,0.00002917223,0.00002819625,2.167107e-7,0.00008686788,0.000004160579,0.9958993,0.00003732646,0.002017559,0.0007953311],"study_design_scores_gemma":[0.0002122743,0.00007575484,0.01473997,0.000009354109,0.00001754274,0.00000995541,0.00006143063,0.00006236057,0.94072,0.000009990118,0.04398464,0.00009677019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961353,0.001936306,0.00111096,0.00006833426,0.000399531,0.00005456169,0.00001799387,0.00001285103,0.0002641858],"genre_scores_gemma":[0.9966578,0.0003639005,0.001808417,0.00003869774,0.0005167348,0.000001893373,0.00003106961,0.00001115111,0.0005702885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05517928,"threshold_uncertainty_score":0.3213863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007796700263631331,"score_gpt":0.264097523210052,"score_spread":0.2563008229464207,"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."}}