{"id":"W2114971758","doi":"10.1109/iscas.2011.5938203","title":"Machine-learning framework for automatic netlist creation","year":2011,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; CMC Microsystems","keywords":"Netlist; Computer science; Set (abstract data type); Electronic circuit; Design flow; Computer architecture; Electronic design automation; Computer engineering; Embedded system; Programming language; Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00170789,0.0009657674,0.000822134,0.001690021,0.0007076174,0.001628803,0.002626083,0.0009533959,0.005747409],"category_scores_gemma":[0.003304488,0.0004874641,0.001166023,0.000905098,0.0009474646,0.001869154,0.001101869,0.001611126,0.002301869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037382,"about_ca_system_score_gemma":0.001363264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001994077,"about_ca_topic_score_gemma":0.003612091,"domain_scores_codex":[0.999157,0.0001685094,0.00006643212,0.0001870038,0.000359654,0.00006135886],"domain_scores_gemma":[0.9988626,0.0005628403,0.00009311603,0.0002047438,0.0002330845,0.00004366037],"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":[0.0001492448,0.000143885,0.0006649854,0.0005099746,0.0001023911,0.0003060724,0.0001654813,0.2217883,0.01972799,0.1375225,0.009540272,0.6093789],"study_design_scores_gemma":[0.00002413922,0.00004082101,0.0001240805,0.00004378332,0.0000172285,0.0001686281,0.00001539935,0.9283368,0.01201137,0.04223525,0.0169595,0.00002305345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003706192,0.00004737811,0.9973138,0.00001909818,0.000006272065,0.00001910845,0.00003654566,0.001892444,0.0002947265],"genre_scores_gemma":[0.02804118,0.0001189618,0.9699504,0.00005526715,0.00002359759,0.0001295854,0.0003743993,0.0002453084,0.001061323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005747409,"threshold_uncertainty_score":0.01922697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02209369211936669,"score_gpt":0.2385453208108897,"score_spread":0.2164516286915231,"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."}}