{"id":"W4244769347","doi":"10.7287/peerj.preprints.26593v1","title":"MIPhy: Identify and quantify rapidly evolving members of large gene families","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Phylogenetic tree; Gene duplication; Biology; Gene; Gene family; Phylogenetics; Caenorhabditis; Evolutionary biology; Lineage (genetic); Genetics; Clade; Caenorhabditis elegans; Phenotype; Organism; Computational biology; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001826024,0.001165014,0.0008370808,0.00429139,0.0007540219,0.001246636,0.001116102,0.0009642715,0.005072205],"category_scores_gemma":[0.005176539,0.0007978646,0.001163718,0.002379837,0.0005210678,0.001637975,0.001298711,0.001261868,0.002622739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005311235,"about_ca_system_score_gemma":0.0005405337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001310869,"about_ca_topic_score_gemma":0.001391793,"domain_scores_codex":[0.9990971,0.00008631419,0.0000607046,0.0003657962,0.0003035106,0.00008659682],"domain_scores_gemma":[0.9977875,0.001086833,0.0005481193,0.0002752104,0.0001648269,0.0001375257],"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":[0.001815583,0.0003606721,0.170599,0.002008101,0.0009769014,0.0008122381,0.001581563,0.02893625,0.4314205,0.007744069,0.04597865,0.3077665],"study_design_scores_gemma":[0.0002412691,0.0005792102,0.2047928,0.0002203135,0.0004294689,0.002483469,0.000616301,0.4729557,0.2369717,0.01347902,0.06687999,0.0003506504],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2967397,0.001337636,0.5775099,0.0003713482,0.0001046647,0.00035019,0.05198044,0.06663002,0.004976091],"genre_scores_gemma":[0.2152928,0.0006343691,0.7405689,0.0003348698,0.00008173657,0.00116112,0.03437164,0.004951308,0.002603192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005072205,"threshold_uncertainty_score":0.01696819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009660969295565,"score_gpt":0.2689234552241906,"score_spread":0.258826845531235,"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."}}