{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002226733,0.001009596,0.0008189483,0.005560142,0.0008925752,0.001163207,0.001047909,0.001095602,0.002519195],"category_scores_gemma":[0.00819035,0.000591392,0.001031588,0.003916909,0.0006653976,0.002024625,0.001635779,0.001247447,0.001189518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004660562,"about_ca_system_score_gemma":0.0004385741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008415607,"about_ca_topic_score_gemma":0.0007089319,"domain_scores_codex":[0.9987908,0.0001980648,0.00009076607,0.0004088626,0.0003970663,0.0001145359],"domain_scores_gemma":[0.9960515,0.002024749,0.001069982,0.0004094925,0.0002404542,0.0002039007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00162713,0.0002965042,0.3005753,0.001557655,0.0009689904,0.0008985941,0.002083891,0.04422035,0.3604631,0.0109506,0.01165264,0.2647053],"study_design_scores_gemma":[0.0001106371,0.0005957377,0.2601469,0.0001518308,0.00043646,0.003265435,0.0007768314,0.5322098,0.1439681,0.02134939,0.03671689,0.000272034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4819206,0.002221256,0.4826441,0.0003424217,0.00006095028,0.0002222734,0.01690472,0.01226685,0.003416752],"genre_scores_gemma":[0.4906427,0.000845025,0.4885672,0.000277832,0.0001039764,0.0006382782,0.01573193,0.001598824,0.001594096],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005560142,"threshold_uncertainty_score":0.01177627,"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."}}