{"id":"W2486243297","doi":"10.7146/brics.v9i51.21766","title":"Computing Refined Buneman Trees in Cubic Time","year":2002,"lang":"en","type":"article","venue":"BRICS Report Series","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Tree (set theory); Pairwise comparison; Measure (data warehouse); Algorithm; Set (abstract data type); Combinatorics; Binary tree; Weight-balanced tree; Enhanced Data Rates for GSM Evolution; Discrete mathematics; Running time; Computer science; Data mining; Binary search tree; Artificial intelligence; Statistics","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":[0.0001380245,0.0001343022,0.0001744329,0.00003985386,0.00008042291,0.00002116339,0.0001028803,0.00007902226,0.00002855696],"category_scores_gemma":[0.00008857226,0.0001350896,0.00006005544,0.0001064582,0.00006450908,0.000001011012,0.0001416109,0.00005042007,0.00001928491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009926194,"about_ca_system_score_gemma":0.00001625431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003056078,"about_ca_topic_score_gemma":0.0001241778,"domain_scores_codex":[0.9990696,0.0000197814,0.000302104,0.0003012766,0.00008951114,0.000217766],"domain_scores_gemma":[0.9994888,0.000007679258,0.0001117949,0.0002989563,0.00005349751,0.00003929854],"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":[0.0000363161,0.0001417994,0.0522184,0.00002219653,0.000105978,0.0004917444,0.0003952428,0.0006282363,0.9300671,0.0001312342,0.01034478,0.005417021],"study_design_scores_gemma":[0.001092148,0.0006302299,0.2605281,0.00004528783,0.00004473674,0.002647498,0.0003037459,0.001058,0.1116186,0.0005361275,0.6203679,0.001127603],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867423,0.00177566,0.00004183441,0.0003054698,0.0001224918,0.00008964245,0.000004355826,0.000007981889,0.01091027],"genre_scores_gemma":[0.989441,0.0003413921,0.0009715108,0.0001010878,0.000149492,0.000006008823,0.00003048274,0.00001832762,0.008940722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8184484,"threshold_uncertainty_score":0.5508794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375843978592752,"score_gpt":0.2229741537553361,"score_spread":0.2092157139694085,"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."}}