{"id":"W2007402758","doi":"10.1198/106186001317114901","title":"Computational Algorithms for Censored-Data Problems Using Intersection Graphs","year":2001,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Censoring (clinical trials); Mathematics; Algorithm; Bivariate analysis; Intersection (aeronautics); Intersection graph; Graph; Nonparametric statistics; Computer science; Mathematical optimization; Discrete mathematics; Statistics; Line graph","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":[0.0006836241,0.0001406142,0.0002576222,0.0002414161,0.0001870386,0.0001573276,0.0004169651,0.00006734417,0.000003520767],"category_scores_gemma":[0.00009990378,0.0001178538,0.00007401053,0.0003409556,0.0001065813,0.0004572747,0.0001092785,0.0002028847,2.930445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002189191,"about_ca_system_score_gemma":0.0001102636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006768539,"about_ca_topic_score_gemma":0.000002287989,"domain_scores_codex":[0.9984704,0.0001009409,0.0005724918,0.0002510243,0.000419735,0.000185425],"domain_scores_gemma":[0.9979511,0.0007063116,0.000371521,0.0001280543,0.0006785521,0.0001644083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006212935,0.0001591739,0.0004614774,0.00004466229,0.000122384,0.0000349173,0.000180084,0.05837135,0.0000206262,0.7454051,0.001480579,0.1936575],"study_design_scores_gemma":[0.0004011035,0.0001383076,0.001567462,0.00002088673,0.00001938739,0.0004290204,0.000003778672,0.500366,8.256669e-7,0.496426,0.0005517068,0.0000755339],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003287562,0.0001997331,0.995171,0.0006550979,0.0003840639,0.0001323319,0.0001395685,0.00001462522,0.0000160136],"genre_scores_gemma":[0.07841685,0.00005016488,0.9210658,0.0002460568,0.0001593336,0.00000128634,0.00004527769,0.000008545439,0.000006665371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4419946,"threshold_uncertainty_score":0.4805937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05423319711498033,"score_gpt":0.3320901280037434,"score_spread":0.2778569308887631,"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."}}