{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008352077,0.0006804901,0.00107951,0.001051141,0.0008256744,0.00198617,0.001398655,0.0007668059,0.008944589],"category_scores_gemma":[0.007975588,0.0004796784,0.0009085022,0.001918672,0.0006672136,0.004278016,0.002240222,0.001184569,0.002205493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007198,"about_ca_system_score_gemma":0.00163265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004333043,"about_ca_topic_score_gemma":0.01040328,"domain_scores_codex":[0.9988664,0.0001986538,0.00009960334,0.0002474515,0.0004345495,0.0001533762],"domain_scores_gemma":[0.9954832,0.002740518,0.0002400951,0.0007675258,0.0006072081,0.0001614448],"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":[0.0007288617,0.0001601014,0.003751159,0.0006159626,0.0001099424,0.0003040604,0.0006755078,0.1968486,0.01227841,0.09668995,0.0181834,0.669654],"study_design_scores_gemma":[0.0001585417,0.0001016891,0.001287287,0.00004209602,0.00003545774,0.000239265,0.000372221,0.720564,0.005326471,0.254753,0.01707711,0.00004297115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1092109,0.0005758347,0.8720232,0.0007217493,0.00007084123,0.0001884028,0.001094344,0.005410429,0.01070425],"genre_scores_gemma":[0.1953073,0.0002934253,0.7955607,0.000131102,0.00003206269,0.0002161149,0.003402741,0.0005604837,0.00449609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008944589,"threshold_uncertainty_score":0.02992266,"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."}}