{"id":"W4242981398","doi":"10.1109/asonam.2016.7752211","title":"Tradeoffs between density and size in extracting dense subgraphs: A unified framework","year":2016,"lang":"en","type":"article","venue":"2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Maximization; Range (aeronautics); Quadratic equation; Generalization; Theoretical computer science; Mathematical optimization; Mathematics","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":[0.001095808,0.0002795746,0.0005453916,0.0003849484,0.0002284011,0.0002039235,0.0009318783,0.0002078141,0.00004524102],"category_scores_gemma":[0.000757591,0.0002281147,0.0001322212,0.001231454,0.0003227745,0.0009024314,0.0002801905,0.0004725566,0.000002643356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089916,"about_ca_system_score_gemma":0.00005954287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003152491,"about_ca_topic_score_gemma":0.0004532421,"domain_scores_codex":[0.9972979,0.0003095592,0.0005219753,0.0008365221,0.0005052202,0.0005287645],"domain_scores_gemma":[0.9957775,0.003235752,0.0003071881,0.0003572584,0.0001792405,0.0001430598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001944031,0.00009300419,0.4791647,0.00001001116,0.0002962687,0.00006203156,0.00144225,0.0004458562,0.0002698429,0.1973119,0.00003095153,0.3206788],"study_design_scores_gemma":[0.002541832,0.0002427788,0.5263683,0.001133798,0.0001427652,0.00001058439,0.001429021,0.06116822,0.0002245737,0.404372,0.001026791,0.001339351],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6370581,0.0005328341,0.3594589,0.001572035,0.0002691712,0.0001461869,0.00001096876,0.000047806,0.0009039366],"genre_scores_gemma":[0.9834782,0.003064362,0.01286233,0.0001164376,0.0002510429,0.00002442147,0.000003616167,0.00001375036,0.0001858805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3465966,"threshold_uncertainty_score":0.9302248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0412797631814928,"score_gpt":0.3476045432026985,"score_spread":0.3063247800212057,"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."}}