{"id":"W2135011160","doi":"10.1109/tvlsi.2005.844286","title":"Physical resource binding for a coarse-grain reconfigurable array using evolutionary algorithms","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Genetic algorithm; Kernel (algebra); Evolutionary algorithm; Resource (disambiguation); Algorithm; Computer engineering; Theoretical computer science; Mathematics; Artificial intelligence; Machine learning","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","sts"],"consensus_categories":[],"category_scores_codex":[0.0005972529,0.0003909547,0.0004208584,0.0004069642,0.00132713,0.00029705,0.0006336356,0.0002161021,0.00002862094],"category_scores_gemma":[0.00001188,0.0003834783,0.0003735818,0.0008965424,0.00008889913,0.001328302,0.000003747901,0.0004419027,0.0001800688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006018278,"about_ca_system_score_gemma":0.0001900751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001329725,"about_ca_topic_score_gemma":0.000087089,"domain_scores_codex":[0.9970275,0.000205678,0.0006955238,0.0008774531,0.0005557487,0.0006380707],"domain_scores_gemma":[0.9981291,0.0002952588,0.0002583723,0.0007710474,0.0003231298,0.0002230502],"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":[0.0002406469,0.006512948,0.00001950733,0.0002421343,0.0005043864,0.00001055618,0.008619065,0.5765606,0.2120007,0.03910055,0.02478694,0.131402],"study_design_scores_gemma":[0.000744314,0.0001479941,0.000007222938,0.0001593058,0.00003938247,0.00008252588,0.0009363916,0.9348289,0.02012575,0.0002325391,0.04226809,0.0004275332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007503815,0.0001204131,0.9872831,0.001036998,0.001282025,0.001161185,0.0004571217,0.0004378382,0.0007175669],"genre_scores_gemma":[0.8747435,0.00001939399,0.1162428,0.0002065841,0.001113471,0.001110923,0.0001021407,0.00007033916,0.006390781],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8710402,"threshold_uncertainty_score":0.999973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02287739560843922,"score_gpt":0.2684129088326981,"score_spread":0.2455355132242589,"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."}}