{"id":"W2155112903","doi":"10.1109/glsv.1991.143948","title":"An efficient tabu search algorithm for graph bisectioning","year":2002,"lang":"en","type":"article","venue":"","topic":"Interconnection Networks and Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Tabu search; Guided Local Search; Computer science; Graph; Algorithm; Set (abstract data type); Search algorithm; Hill climbing; 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":[0.0003435451,0.00007572596,0.00008834073,0.0001202755,0.0002729265,0.0002513762,0.0003234973,0.00004228169,0.00009440526],"category_scores_gemma":[0.000004059328,0.00006321786,0.00007079636,0.0003346788,0.00001376031,0.0001812076,0.00003256445,0.00006646803,0.00004578187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002528899,"about_ca_system_score_gemma":0.000005635082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006278777,"about_ca_topic_score_gemma":0.00001121485,"domain_scores_codex":[0.9990906,0.00004687052,0.0001563184,0.0002915587,0.0001620227,0.000252696],"domain_scores_gemma":[0.9993997,0.00005061408,0.00002508224,0.0002995906,0.0001405598,0.00008440303],"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":[0.000003165878,0.0003794524,0.0001477213,0.00001784962,0.00004430924,0.000007230918,0.002984201,0.1031777,0.000465345,0.1390778,0.02069037,0.7330049],"study_design_scores_gemma":[0.0001726596,0.0001518783,0.00003535897,0.000006111074,9.719578e-7,0.00002163157,0.00008408044,0.9949957,0.0004159126,0.0001071303,0.003914182,0.00009445206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003860924,0.00004591878,0.9904529,0.0001574302,0.001446275,0.0001897754,8.96742e-7,0.0002157147,0.003630105],"genre_scores_gemma":[0.9067265,0.000003299724,0.08994668,0.0001988708,0.0003662028,0.00004254076,0.000001054574,0.000008550846,0.002706325],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9028655,"threshold_uncertainty_score":0.2577949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03160924119782899,"score_gpt":0.263572433394145,"score_spread":0.231963192196316,"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."}}