{"id":"W2177375219","doi":"10.1093/molbev/msv223","title":"Phenoscape: Identifying Candidate Genes for Evolutionary Phenotypes","year":2015,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Division of Emerging Frontiers; National Evolutionary Synthesis Center; National Human Genome Research Institute; Academy of Natural Sciences of Drexel University; National Institutes of Health; National Science Foundation","keywords":"Biology; Phenotype; Candidate gene; Gene; Ictalurus; Genetics; Evolutionary biology; Model organism; Phenotypic trait; Genetic architecture; Catfish; Computational biology; Fish <Actinopterygii>","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":[0.0009594118,0.0008350215,0.0006252531,0.002301255,0.0006472992,0.0008913293,0.001181393,0.0007368483,0.004768089],"category_scores_gemma":[0.002178189,0.0003868135,0.0007291753,0.001417132,0.000426618,0.00142732,0.001208049,0.0006260482,0.001163929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004976305,"about_ca_system_score_gemma":0.0008324307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001724855,"about_ca_topic_score_gemma":0.003244108,"domain_scores_codex":[0.9997172,0.00002906727,0.00003908979,0.0001056464,0.00008944439,0.00001952687],"domain_scores_gemma":[0.998694,0.0006912379,0.0002695776,0.0001916867,0.00008987974,0.00006372596],"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":[0.003138208,0.0006923898,0.1278848,0.006850124,0.0007769795,0.008952297,0.001897683,0.01961034,0.5363563,0.02530973,0.02720645,0.2413247],"study_design_scores_gemma":[0.0004076034,0.001009994,0.2246439,0.001186699,0.001614625,0.009282967,0.00222665,0.15655,0.3051597,0.04357322,0.2539442,0.0004005567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4369835,0.001995414,0.3865604,0.001141188,0.0002526089,0.0006020708,0.1270937,0.03290679,0.01246429],"genre_scores_gemma":[0.4801479,0.002122656,0.3541826,0.0003773512,0.00006444824,0.0006554427,0.1572999,0.001936758,0.003212855],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004768089,"threshold_uncertainty_score":0.01595092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585099537588687,"score_gpt":0.2753857895561657,"score_spread":0.2595347941802788,"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."}}