{"id":"W3092159560","doi":"10.1093/bioinformatics/btaa882","title":"DoubleRecViz: a web-based tool for visualizing transcript–gene–species tree reconciliation","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Phylogenetic tree; Tree (set theory); Visualization; Toolbox; Python (programming language); Phylogenetic network; Biology; Computer science; Phylogenetics; Gene; Computational biology; Data mining; Genetics; Programming language","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":[0.0001266238,0.0001707738,0.0001761199,0.000026311,0.0001160147,0.00004659154,0.0001571675,0.0001081292,0.000009154004],"category_scores_gemma":[0.00008848833,0.0001627029,0.0001510303,0.0000748834,0.00003936296,0.000002606766,0.00003374256,0.00003943389,0.000009950639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001374623,"about_ca_system_score_gemma":0.0001133162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001471458,"about_ca_topic_score_gemma":0.00001195246,"domain_scores_codex":[0.9991239,0.00001270647,0.0003626463,0.0001664137,0.0001062913,0.0002279911],"domain_scores_gemma":[0.9995031,0.00001938592,0.0001161559,0.0001749357,0.000111849,0.00007452588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002648972,0.0000339969,0.00156258,0.0002413972,0.0001210325,2.972369e-7,0.0009291142,0.0005466702,0.9757211,0.0002867091,0.01196119,0.008330991],"study_design_scores_gemma":[0.003180354,0.0008758531,0.001391742,0.00001532473,0.00007238425,0.000001990376,0.0004986306,0.04000608,0.4370838,0.00004113395,0.5163321,0.0005006653],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023274,0.0008599337,0.08957834,0.001676213,0.0004501012,0.001047287,0.0003473187,0.00003635092,0.003677078],"genre_scores_gemma":[0.9658306,0.000183473,0.03055372,0.00256768,0.0003500032,0.00007810331,0.0002450873,0.00002565403,0.0001657211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5386374,"threshold_uncertainty_score":0.6634831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04008395639011875,"score_gpt":0.2512190770131791,"score_spread":0.2111351206230604,"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."}}