{"id":"W2608342387","doi":"10.1090/crmp/045/02","title":"0-1 semidefinite programming for graph-cut clustering: Modelling and approximation","year":2008,"lang":"en","type":"book-chapter","venue":"CRM proceedings & lecture notes","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Semidefinite programming; Cluster analysis; Semidefinite embedding; Computer science; Mathematical optimization; Combinatorics; Mathematics; Artificial intelligence; Quadratically constrained quadratic program","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008370193,0.0005867219,0.0005093665,0.0003054806,0.000197638,0.0001714654,0.000152987,0.0006227704,0.000002934264],"category_scores_gemma":[0.00003398381,0.000600416,0.0001661174,0.00005958359,0.00007969323,0.0001554682,0.00005735971,0.0005092549,0.000001958725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005697376,"about_ca_system_score_gemma":0.00001469981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003113174,"about_ca_topic_score_gemma":0.000002660623,"domain_scores_codex":[0.99857,0.00000122747,0.0003586152,0.0004900611,0.0002008362,0.0003792632],"domain_scores_gemma":[0.9992828,0.00008327581,0.000158377,0.0001401109,0.0002459534,0.00008945999],"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":[0.0003879573,0.00007999337,0.00007801902,0.009106816,0.001751375,0.00005204216,0.01226337,0.2173693,0.01696661,0.230689,0.003517326,0.5077381],"study_design_scores_gemma":[0.0004095522,0.000200216,0.000001771192,0.002037627,0.0003020362,0.000212161,0.00001469803,0.4780068,0.02191216,0.3885983,0.1066242,0.001680573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009498305,0.005771777,0.8774182,0.00007203461,0.0002300589,0.001428849,0.00001911422,0.002632251,0.1114779],"genre_scores_gemma":[0.9457044,0.003388573,0.04869066,0.0001358374,0.0009552017,0.0001984249,0.0001207787,0.0005165266,0.0002896263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9447545,"threshold_uncertainty_score":0.9996447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03124053358594353,"score_gpt":0.2179380810176818,"score_spread":0.1866975474317382,"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."}}