{"id":"W4296079037","doi":"10.29173/mocs273","title":"Data-driven cycle time prediction of fitting and welding stations in steel fabrication","year":2022,"lang":"en","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Benchmark (surveying); Computer science; Reliability (semiconductor); Process (computing); Welding; Fabrication; Predictive modelling; Reliability engineering; Industrial engineering; Data mining; Engineering; Machine learning; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008654536,0.0006994901,0.0004476327,0.001092006,0.0002595819,0.0008395551,0.000668986,0.0008051306,0.001198196],"category_scores_gemma":[0.003762152,0.0004341674,0.0005619992,0.001140407,0.0002336372,0.000640342,0.0003449014,0.0009169399,0.0003887787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009319547,"about_ca_system_score_gemma":0.0009667036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02528365,"about_ca_topic_score_gemma":0.02162332,"domain_scores_codex":[0.9997349,0.00004696305,0.00001657204,0.00008155785,0.00007990566,0.00004017304],"domain_scores_gemma":[0.9981263,0.001200108,0.0001994211,0.00009704442,0.0003116485,0.00006543162],"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":[0.0001200519,0.0001016426,0.01098484,0.00004952462,0.00002043869,0.00004337307,0.00003214359,0.9719394,0.001073017,0.0002984524,0.0002992639,0.01503785],"study_design_scores_gemma":[0.000002188759,0.00002000977,0.003041347,0.000003224747,0.000003719917,0.000004831454,0.00001133521,0.9958023,0.0008475768,0.0001416676,0.0001166569,0.000005045707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9275037,0.0002841327,0.06873382,0.0001698784,0.00004313419,0.00005060319,0.000877902,0.000649558,0.00168729],"genre_scores_gemma":[0.99145,0.00008412777,0.007005819,0.00001252193,0.000004798109,0.0000278523,0.0007471294,0.00002595167,0.0006417678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02528365,"threshold_uncertainty_score":0.05027294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056133184248477,"score_gpt":0.1946477596264216,"score_spread":0.1840864277839368,"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."}}