{"id":"W1984131666","doi":"10.1080/03052150801901475","title":"VLSI floorplan repair using dynamic white-space management, constraint graphs, and linear programming","year":2008,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Floorplan; Very-large-scale integration; White spaces; Computer science; Constraint (computer-aided design); Mathematical optimization; Linear programming; Integrated circuit layout; Range (aeronautics); High-level synthesis; Computer engineering; Algorithm; Integrated circuit; Mathematics; Engineering; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000921754,0.0002094828,0.0001624696,0.0002508854,0.00008699953,0.0000271511,0.00006740309,0.0001002093,0.000009353656],"category_scores_gemma":[0.00001071152,0.0002447253,0.00004944199,0.0003116473,0.00003382932,0.0001861124,0.00002541117,0.0001410141,0.000001421869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006353595,"about_ca_system_score_gemma":0.000005260219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003191742,"about_ca_topic_score_gemma":3.715303e-7,"domain_scores_codex":[0.9992082,0.00000860124,0.0002054876,0.0002024545,0.0001120373,0.0002632584],"domain_scores_gemma":[0.9996852,0.00001233606,0.00002588682,0.0001735379,0.00002645012,0.00007656975],"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":[0.000001543168,0.000008168585,0.0003750362,0.0001352922,0.00004493385,0.000031866,0.0001362886,0.9977192,0.0005477637,0.0001783649,0.00007937531,0.000742139],"study_design_scores_gemma":[0.0001741521,0.00001972644,0.0002247931,0.00007939102,0.00002227244,0.000096433,0.00002867174,0.9982121,0.0003243403,0.000006568539,0.000542362,0.0002691881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02433174,0.0003923563,0.9712886,0.000009244738,0.0001353106,0.000316414,0.000004312203,0.003234513,0.000287488],"genre_scores_gemma":[0.3630696,0.0004932521,0.6362468,0.00000736592,0.00002010074,0.00002273917,0.00002943395,0.00006330073,0.0000473987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3387379,"threshold_uncertainty_score":0.9979607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008025436742093614,"score_gpt":0.1966417485665126,"score_spread":0.188616311824419,"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."}}