{"id":"W2938668898","doi":"10.29173/mocs10","title":"Production Monitoring and Process Improvement for Floor Panel Manufacturing","year":2016,"lang":"en","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"BIM and Construction Integration","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Installation; Process (computing); Factory (object-oriented programming); Modular design; Bridge (graph theory); Production (economics); Engineering; Production line; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001352079,0.0002637783,0.0002126779,0.0001848925,0.0002564216,0.0001360822,0.00006494932,0.0001450669,0.0000136618],"category_scores_gemma":[0.00002589603,0.0002152377,0.00005079541,0.0001022068,0.0001347134,0.0008136057,0.00002174703,0.0001184366,0.000003306687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008161006,"about_ca_system_score_gemma":0.00001236955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005008743,"about_ca_topic_score_gemma":0.000002270851,"domain_scores_codex":[0.9987652,0.000003245304,0.0003161891,0.000448945,0.0001696585,0.0002967028],"domain_scores_gemma":[0.9994999,0.00001082237,0.00008533579,0.00008873986,0.0002003273,0.000114891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005951877,0.00001098323,0.01446356,0.0005534522,0.00007920715,3.767911e-7,0.0002709292,0.00003519798,0.1986022,0.001172466,0.0000981812,0.784654],"study_design_scores_gemma":[0.001265216,0.0001269757,0.009500699,0.0003255176,0.00009747065,0.0001215466,0.002359043,0.001479069,0.9756535,0.003276394,0.005215857,0.0005786961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880517,0.0002897115,0.008982272,0.0002127553,0.001347179,0.0005196335,0.00001288818,0.0003615324,0.0002223269],"genre_scores_gemma":[0.9955451,0.0003579162,0.002988599,0.000008322093,0.0006113264,0.0001957632,0.000003641822,0.00004008098,0.0002492383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7840753,"threshold_uncertainty_score":0.877714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111884183171663,"score_gpt":0.2035844969300306,"score_spread":0.192465655098314,"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."}}