{"id":"W86723469","doi":"10.22260/isarc2014/0034","title":"Wood-Frame Wall Panel Sequencing Based on Discrete-Event Simulation and Particle Swarm Optimization","year":2014,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle swarm optimization; Frame (networking); Event (particle physics); Process (computing); Doors; Computer science; Task (project management); Algorithm; Engineering; Discrete event simulation; Sequence (biology); Real-time computing; Simulation; Mechanical engineering; Systems 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":[0.0001877524,0.000128526,0.0001229601,0.00004256252,0.00008328312,0.00005791476,0.0001241466,0.00006127225,0.00001268596],"category_scores_gemma":[0.0001068665,0.00009970523,0.0000369259,0.0001232507,0.00002395624,0.0001833643,0.00003051825,0.00009844544,0.000001423065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000548739,"about_ca_system_score_gemma":0.000006403284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007521158,"about_ca_topic_score_gemma":5.625619e-7,"domain_scores_codex":[0.999295,0.000004904003,0.0001932364,0.0001538474,0.0001917125,0.0001613257],"domain_scores_gemma":[0.9996586,0.0000515842,0.00008455701,0.00008560504,0.00007303163,0.00004663817],"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":[0.00001262367,0.000009379885,0.000591178,0.0001879497,0.000005831353,1.758475e-8,0.0003060959,0.9970352,0.0007371154,0.0004126941,0.00001463818,0.0006872428],"study_design_scores_gemma":[0.0002479335,0.00004015501,0.0005683946,0.0001053683,0.00001942313,2.817515e-7,0.00004664241,0.9612647,0.03709711,0.0003892566,0.0001032482,0.0001175214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8672071,0.00003004224,0.1289256,0.000474872,0.000146084,0.0003320837,0.000002653109,0.000234188,0.002647437],"genre_scores_gemma":[0.9957389,0.000008943691,0.004031378,0.00009727022,0.00004262926,0.000011914,0.000002302911,0.00002779097,0.00003883798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1285319,"threshold_uncertainty_score":0.4065861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01600427153278436,"score_gpt":0.218065047427813,"score_spread":0.2020607758950287,"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."}}