{"id":"W2800418272","doi":"10.1145/3158215","title":"Eh?Legalizer","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Design Automation of Electronic Systems","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Alberta Innovates - Technology Futures; CMC Microsystems","keywords":"Legalization; Computer science; Robustness (evolution); Scalability; Routing (electronic design automation); Standard cell; Floorplan; Mathematical optimization; Process (computing); Parallel computing; Algorithm; Embedded system; Mathematics; Integrated circuit","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":[0.0004084763,0.0006907469,0.0003541729,0.0008736165,0.0003977434,0.000762392,0.001228711,0.0005617574,0.03046382],"category_scores_gemma":[0.00174173,0.0002609459,0.0004378072,0.0004648999,0.0004434771,0.001025795,0.001103773,0.000609305,0.004312634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006031549,"about_ca_system_score_gemma":0.001271008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001986941,"about_ca_topic_score_gemma":0.005062892,"domain_scores_codex":[0.999577,0.00004597564,0.00002766074,0.00008892515,0.0002049149,0.00005545832],"domain_scores_gemma":[0.9993836,0.0001838312,0.00005987526,0.0001638641,0.0001887449,0.00002009031],"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":[0.0002945322,0.0001244943,0.001453236,0.000532722,0.00005081332,0.0004355882,0.0002577493,0.05263799,0.04057219,0.03792184,0.05454681,0.811172],"study_design_scores_gemma":[0.000243046,0.0003069991,0.001268103,0.0001163379,0.00005807832,0.0008295204,0.0002574509,0.6606183,0.1324114,0.03025281,0.1735798,0.00005814216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02417496,0.0003291115,0.9206922,0.0002616305,0.0001625816,0.0003807106,0.0008403724,0.02189179,0.03126659],"genre_scores_gemma":[0.1794738,0.0002778776,0.7904039,0.0002976173,0.00004070373,0.0002839052,0.002818532,0.003151406,0.02325235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03046382,"threshold_uncertainty_score":0.1019116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01784580412577238,"score_gpt":0.2380836591198523,"score_spread":0.2202378549940799,"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."}}