{"id":"W4323240958","doi":"10.5220/0011697100003414","title":"GPTree: Generator of Phylogenetic Trees with Overlapping and Biological Events for Supertree Inference","year":2023,"lang":"en","type":"article","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Supertree; Phylogenetic tree; Computer science; Generator (circuit theory); Inference; Artificial intelligence; Biology; Power (physics); Physics; Genetics","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":[0.002670398,0.002591225,0.001778193,0.002971261,0.001337446,0.002889275,0.005189393,0.002801502,0.03151378],"category_scores_gemma":[0.01872474,0.002325159,0.002574677,0.003635716,0.0008103066,0.004010653,0.002665607,0.005212975,0.01443545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001755,"about_ca_system_score_gemma":0.001935201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003073378,"about_ca_topic_score_gemma":0.005418966,"domain_scores_codex":[0.9991721,0.0002310012,0.00007164825,0.0002994454,0.0001442271,0.00008157539],"domain_scores_gemma":[0.9960433,0.002466358,0.0001423485,0.0006979975,0.0004537186,0.0001963184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002488665,0.0005024612,0.00710303,0.004413084,0.0009040637,0.001300661,0.001875216,0.07139917,0.01976378,0.02779691,0.4499176,0.4125353],"study_design_scores_gemma":[0.0009006315,0.0001856992,0.001628629,0.0003799998,0.0002767772,0.0006982423,0.0002971334,0.8122609,0.01641805,0.08339179,0.08339833,0.000163932],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009628676,0.000380518,0.6452965,0.00034587,0.0002662842,0.0004766494,0.03020401,0.311258,0.002143455],"genre_scores_gemma":[0.06454165,0.0004549754,0.8100342,0.0002843262,0.00009616549,0.00145122,0.07939819,0.04197538,0.001763956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03151378,"threshold_uncertainty_score":0.1054241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894166408240295,"score_gpt":0.2600672045218732,"score_spread":0.2311255404394703,"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."}}