{"id":"W2341168710","doi":"10.1371/journal.pone.0159559","title":"Efficient Gene Tree Correction Guided by Genome Evolution","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":51,"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 Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Université de Montréal; Agence Nationale de la Recherche; Compute Canada","keywords":"Ensembl; Tree (set theory); Genome; Context (archaeology); Gene duplication; Biology; Computational biology; Gene; Comparative genomics; Genomics; Genetics; Computer science; Mathematics; Paleontology","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":[0.00133906,0.001012146,0.0009518448,0.001725684,0.000928334,0.001058608,0.001572176,0.0008695385,0.004965839],"category_scores_gemma":[0.009829568,0.0005269191,0.0007837401,0.002059959,0.0006130812,0.001519625,0.001131553,0.001735891,0.003152103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005808611,"about_ca_system_score_gemma":0.001370152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00185398,"about_ca_topic_score_gemma":0.003586638,"domain_scores_codex":[0.9991345,0.000158266,0.0000455964,0.000320455,0.0002719422,0.00006930668],"domain_scores_gemma":[0.9964227,0.001270851,0.0003725254,0.0009054063,0.000910457,0.0001179774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00108849,0.0001843256,0.02369175,0.001040175,0.0002073436,0.0008974422,0.0007562704,0.04826662,0.1434523,0.01754967,0.01919458,0.7436711],"study_design_scores_gemma":[0.0001707871,0.000228646,0.01349124,0.0001919075,0.0002000559,0.002384499,0.0003380707,0.7788258,0.1089113,0.05217332,0.04297308,0.0001112807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08053318,0.000669769,0.9001419,0.0002951062,0.000210031,0.0001256806,0.001858327,0.01417497,0.001990954],"genre_scores_gemma":[0.1683954,0.0002536651,0.820282,0.0001609468,0.0000587949,0.0001000534,0.005753812,0.002535582,0.002459719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004965839,"threshold_uncertainty_score":0.01661241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837330146851857,"score_gpt":0.2016987543822036,"score_spread":0.183325452913685,"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."}}