{"id":"W2162694675","doi":"10.1109/iccd.2008.4751836","title":"A parallel Steiner tree heuristic for macro cell routing","year":2008,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Steiner tree problem; Computer science; Routing (electronic design automation); Tree (set theory); Heuristic; Parameterized complexity; Very-large-scale integration; Macro; Set (abstract data type); Parallel computing; Algorithm; Mathematical optimization; Mathematics; Computer network; Combinatorics; Artificial intelligence; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003633586,0.0004944498,0.000717591,0.0008608203,0.0005164128,0.0004456984,0.0008524351,0.0005314666,0.003110291],"category_scores_gemma":[0.0005883359,0.0003617536,0.0006170587,0.001121218,0.000322482,0.0009696467,0.0005687128,0.0004044992,0.0004799142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005165856,"about_ca_system_score_gemma":0.0008284166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001809919,"about_ca_topic_score_gemma":0.003693606,"domain_scores_codex":[0.9998183,0.00004609519,0.0000112934,0.00003524718,0.00005940302,0.00002974747],"domain_scores_gemma":[0.9997974,0.00008575926,0.00002714937,0.00003081717,0.00003919502,0.00001959778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001935741,0.0001120307,0.0008576664,0.0001448185,0.00006944617,0.0002236454,0.00009275605,0.7670932,0.01391543,0.01158312,0.005202787,0.2005116],"study_design_scores_gemma":[0.00005437279,0.0001622417,0.000274066,0.000009170343,0.00002637727,0.0001388725,0.00003240527,0.9864618,0.003056694,0.006840117,0.002930718,0.00001311856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08097357,0.0006986772,0.9100403,0.0001801077,0.00005718985,0.0002165264,0.0002154333,0.0007080562,0.006910245],"genre_scores_gemma":[0.3541592,0.000427171,0.6390314,0.0001045995,0.00003395553,0.000251944,0.00070604,0.0001801992,0.005105532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003110291,"threshold_uncertainty_score":0.01040494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818645157864015,"score_gpt":0.2069256173150415,"score_spread":0.1887391657364013,"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."}}