{"id":"W1858849203","doi":"10.1109/isqed.2005.126","title":"Two-Dimensional Layout Migration by Soft Constraint Satisfaction","year":2005,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Compaction; Constraint (computer-aided design); Task (project management); Computer science; Process (computing); Computer engineering; Power (physics); Algorithm; Distributed computing; Parallel computing; Engineering; Mechanical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001581254,0.00008813803,0.00007561783,0.00005702196,0.0001070667,0.00009746003,0.0001663941,0.00004064862,0.00006719851],"category_scores_gemma":[0.00001409156,0.00008041155,0.00003177343,0.000122321,0.00002580071,0.0003551251,0.00005611118,0.00007239432,0.00008475058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003071564,"about_ca_system_score_gemma":0.00003657933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001075976,"about_ca_topic_score_gemma":0.00005411754,"domain_scores_codex":[0.9992445,0.00003831446,0.0001733632,0.0002275779,0.0001783644,0.0001378594],"domain_scores_gemma":[0.9995791,0.00004308008,0.00005955349,0.0001918896,0.00007052252,0.00005582348],"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":[0.000007119981,0.0001284843,0.004048427,0.000004408685,0.00002363995,0.000002223167,0.0003791777,0.2678029,0.007095657,0.08437829,0.2526745,0.3834552],"study_design_scores_gemma":[0.000199196,0.0000268857,0.0005389223,0.000005349819,0.000001533879,0.00001660611,0.000003764714,0.9850438,0.009125615,0.0007604878,0.004138094,0.000139793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01420695,0.00004635499,0.9780188,0.002547568,0.00008244047,0.00007726884,0.000001257116,0.0007987708,0.004220582],"genre_scores_gemma":[0.6150708,0.000002151991,0.3835131,0.0008780738,0.00003634414,0.000003547479,0.000006194548,0.000002943442,0.0004868926],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7172409,"threshold_uncertainty_score":0.3279088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059531354780243,"score_gpt":0.2519284542276211,"score_spread":0.2413331406798187,"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."}}