{"id":"W2765676440","doi":"10.1093/molbev/msx283","title":"CompositeSearch: A Generalized Network Approach for Composite Gene Families Detection","year":2017,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"FP7 Ideas: European Research Council","keywords":"Shuffling; Biology; Computational biology; Gene; Genetics; Similarity (geometry); Component (thermodynamics); Homology (biology); Artificial intelligence; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002212848,0.000154656,0.0001721933,0.00003085845,0.0008474107,0.00004056728,0.0001733637,0.0001941383,4.317757e-7],"category_scores_gemma":[0.00004234588,0.0001479917,0.00008944033,0.00002313712,0.0002682449,0.000001389244,0.0002019601,0.00005917334,7.808439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001190074,"about_ca_system_score_gemma":0.00001797717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006836741,"about_ca_topic_score_gemma":0.00002720033,"domain_scores_codex":[0.9990562,0.00007773164,0.0001403778,0.000388334,0.00003936559,0.0002979435],"domain_scores_gemma":[0.9993947,0.00000841542,0.00009887302,0.0003555744,0.00008573115,0.00005674663],"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":[0.0001926514,0.00002240953,0.01766379,0.00001272553,0.0001232917,3.853136e-7,0.00001142557,0.0004800765,0.9784774,0.001233419,0.00006169892,0.001720708],"study_design_scores_gemma":[0.003517829,0.001376089,0.2435762,0.00001147716,0.0001658985,0.00006478882,0.00004817097,0.01032933,0.7199342,0.01062085,0.009557161,0.0007980094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7179564,0.003355849,0.2779276,0.00006638987,0.0001255346,0.0002695367,0.00002575148,0.000004626314,0.0002682641],"genre_scores_gemma":[0.9798256,0.000381116,0.01906057,0.0001057458,0.0002836687,0.00009967067,0.0001734059,0.00001585061,0.00005437661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2618692,"threshold_uncertainty_score":0.6517684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670598537862296,"score_gpt":0.2745868233035209,"score_spread":0.257880837924898,"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."}}