{"id":"W2007078239","doi":"10.1007/s00239-006-0072-4","title":"Using Confidence Set Heuristics During Topology Search Improves the Robustness of Phylogenetic Inference","year":2006,"lang":"en","type":"article","venue":"Journal of Molecular Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Phylogenetic tree; Inference; Tree (set theory); Heuristics; Tree rearrangement; Robustness (evolution); Supertree; Computer science; Algorithm; Set (abstract data type); Maximization; Network topology; Mathematics; Biology; Artificial intelligence; Mathematical optimization; Combinatorics; 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":[0.01847021,0.002309399,0.004483634,0.005761677,0.00221193,0.004745608,0.005148078,0.004629386,0.005552125],"category_scores_gemma":[0.1202748,0.00179575,0.002213799,0.00468656,0.001533727,0.006647357,0.004029939,0.006152753,0.001554357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157482,"about_ca_system_score_gemma":0.003403188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00451118,"about_ca_topic_score_gemma":0.00790544,"domain_scores_codex":[0.9880128,0.007514891,0.0008216672,0.001440569,0.00165591,0.0005542407],"domain_scores_gemma":[0.842928,0.1344012,0.003152511,0.01282254,0.005110427,0.001585262],"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":[0.002054702,0.0006742509,0.01310275,0.000527102,0.00104338,0.0005960535,0.0004637631,0.6256691,0.006924488,0.02284099,0.006144112,0.3199594],"study_design_scores_gemma":[0.0002082683,0.0001619425,0.0006764697,0.00007664042,0.0001615021,0.0002160725,0.00007108883,0.9753096,0.002699677,0.01930982,0.00106434,0.00004469688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1202258,0.001621066,0.8684246,0.0006907956,0.0002666528,0.0002392659,0.0005310412,0.004204476,0.00379629],"genre_scores_gemma":[0.508341,0.0004101256,0.4865071,0.0006111116,0.0002262109,0.0001962291,0.001655723,0.001325907,0.0007265563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01847021,"threshold_uncertainty_score":0.09768099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849437264364496,"score_gpt":0.2769568813293193,"score_spread":0.2584625086856744,"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."}}