{"id":"W2167788667","doi":"10.1109/iwsoc.2005.29","title":"A structure based clustering algorithm with applications to VLSI physical design","year":2005,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cluster analysis; Benchmark (surveying); Computer science; Very-large-scale integration; Suite; Algorithm; Algorithm design; Canopy clustering algorithm; Correlation clustering; Data mining; Artificial intelligence","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":[0.00002785643,0.0001294203,0.0001090544,0.00006422987,0.000036843,0.0000309296,0.0001163474,0.00004158547,0.00005531971],"category_scores_gemma":[9.850869e-7,0.0001039827,0.00002158426,0.000185689,0.000009781459,0.00007569532,0.00001136034,0.00009176983,0.00003558785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004716525,"about_ca_system_score_gemma":0.000009870839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002867652,"about_ca_topic_score_gemma":0.000009267255,"domain_scores_codex":[0.9994992,0.000007066654,0.00007886389,0.0001394693,0.00009848284,0.0001769155],"domain_scores_gemma":[0.9996583,0.00002065393,0.000007013393,0.0002038958,0.00002225046,0.00008783509],"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":[0.000005889984,0.0000328444,0.000005369555,0.00002217249,0.00002143421,0.00000195499,0.0001597024,0.3142832,0.04600615,0.0002072335,0.002838805,0.6364153],"study_design_scores_gemma":[0.0001254298,0.00005962369,0.00002811532,0.000009597113,0.000009108892,0.000004886128,0.000009345825,0.8247288,0.1645013,0.0001089297,0.01022254,0.0001923323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006047427,0.00001190871,0.9960464,0.00008084765,0.000007600252,0.0004448873,0.000008416558,0.001121526,0.001673648],"genre_scores_gemma":[0.4078093,6.039977e-7,0.5916834,0.0001504317,0.0001326994,0.0001378402,0.000003293279,0.00002540392,0.00005699644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.636223,"threshold_uncertainty_score":0.4240292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00938799234256803,"score_gpt":0.2238860305989435,"score_spread":0.2144980382563755,"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."}}