{"id":"W4280560843","doi":"10.23919/date54114.2022.9774530","title":"CR&amp;P: An Efficient Co-operation between Routing and Placement","year":2022,"lang":"en","type":"article","venue":"2022 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Routing (electronic design automation); Physical design; Electronic design automation; Placement; Design flow; CONTEST; Network routing; Place and route; Integer programming; Distributed computing; Embedded system; Circuit design; Algorithm","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002041027,0.0005139663,0.0004739344,0.0006071466,0.0006138369,0.0004358016,0.0003790775,0.0001569974,0.00241838],"category_scores_gemma":[0.0003260482,0.0005986565,0.00005848851,0.0007966098,0.00008773075,0.0004917633,0.0001731299,0.0006775669,0.0005351293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003039714,"about_ca_system_score_gemma":0.0001353665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006164372,"about_ca_topic_score_gemma":0.0001170469,"domain_scores_codex":[0.9959372,0.0008816629,0.001054863,0.0007712679,0.0007629264,0.0005920577],"domain_scores_gemma":[0.998243,0.0003679532,0.0002679785,0.0006899538,0.000216678,0.0002143831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009462949,0.0008898497,0.007630024,0.0003487584,0.00009417554,0.00002133976,0.01120181,0.3579178,0.5752754,0.002834431,0.02240789,0.02128389],"study_design_scores_gemma":[0.005394743,0.001155153,0.04503496,0.0007596525,0.0002916903,0.0002606686,0.001002907,0.4910298,0.03281133,0.001372829,0.4151267,0.005759592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3298905,0.00009222228,0.6645416,0.0001263161,0.0001898285,0.001172183,0.0002471959,0.001746646,0.001993529],"genre_scores_gemma":[0.9705552,0.0001169942,0.02334959,0.0001560334,0.000130193,0.0004041177,0.004238393,0.0001245761,0.0009248402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.641192,"threshold_uncertainty_score":0.9996465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0573484131648651,"score_gpt":0.2814321515290119,"score_spread":0.2240837383641468,"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."}}