{"id":"W2140810179","doi":"10.1093/molbev/msp271","title":"Inferring the Evolutionary History of Gene Clusters from Phylogenetic and Gene Order Data","year":2009,"lang":"en","type":"article","venue":"Molecular Biology and Evolution","topic":"Biochemical Analysis and Sensing Techniques","field":"Nursing","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Gene duplication; Biology; Phylogenetic tree; Gene; Evolutionary biology; Gene cluster; Phylogenetics; Phylogenetic network; Natural selection; Inference; Genetics; Gene family; Molecular evolution; Computational biology; Selection (genetic algorithm); Artificial intelligence; Genome; Computer science","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":[0.001547911,0.0005830575,0.0007692075,0.004009048,0.0009807546,0.001159628,0.0009983791,0.0009239805,0.0009795509],"category_scores_gemma":[0.00728055,0.0007884492,0.000952184,0.002111951,0.0006633736,0.001543205,0.0007528196,0.0009115934,0.0004112308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044053,"about_ca_system_score_gemma":0.001253056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005267479,"about_ca_topic_score_gemma":0.008117408,"domain_scores_codex":[0.9994394,0.0001586618,0.00004156076,0.0001955881,0.0001091228,0.00005559259],"domain_scores_gemma":[0.9972638,0.001878582,0.0002339453,0.000279283,0.0002100575,0.000134335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007141522,0.0002147572,0.1006673,0.0004129997,0.0003561307,0.0004584959,0.001082068,0.5229257,0.03697018,0.010793,0.002064361,0.3233409],"study_design_scores_gemma":[0.00003382977,0.00006072118,0.01343939,0.00004473462,0.0001123646,0.0002853624,0.0002073315,0.9547949,0.007412693,0.02140326,0.002169427,0.00003601865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5387745,0.000862506,0.4560336,0.0003228318,0.00001644525,0.0001052264,0.001234698,0.001424758,0.001225569],"genre_scores_gemma":[0.6146221,0.0004832634,0.3814685,0.00006013602,0.00002094936,0.00006175433,0.002511131,0.0002557944,0.0005163215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005267479,"threshold_uncertainty_score":0.01047361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497437930638748,"score_gpt":0.2530240779077751,"score_spread":0.2380496986013876,"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."}}