{"id":"W2937940533","doi":"10.29173/mocs65","title":"Using noisy RFID for accurately monitoring the assembly line of panel fabrication","year":2017,"lang":"en","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bottleneck; Timestamp; Workstation; Automotive industry; Computer science; Assembly line; Line (geometry); Production line; Reading (process); Downstream (manufacturing); Production (economics); Backup; Real-time computing; Embedded system; Operating system; Automotive engineering; Engineering; Operations management; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002449092,0.0002036617,0.0002545346,0.0001095521,0.0005429746,0.0002420376,0.0002223433,0.0001357444,0.000001859517],"category_scores_gemma":[0.0002053403,0.000183903,0.00006922065,0.0001219725,0.0001152488,0.0007509209,0.00005711161,0.0001382143,0.000001008618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006160918,"about_ca_system_score_gemma":0.00001973355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002792854,"about_ca_topic_score_gemma":0.000002738441,"domain_scores_codex":[0.998978,0.000005238408,0.0003541077,0.0002553847,0.0001762843,0.0002310444],"domain_scores_gemma":[0.9988818,0.00003259045,0.0003042547,0.0002248741,0.0004978254,0.00005869447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001335945,0.00005150943,0.2378148,0.001250882,0.0003091645,0.000001081789,0.001342443,0.06245996,0.5934015,0.003036231,0.0003260313,0.09987283],"study_design_scores_gemma":[0.0008516181,0.00005481458,0.04052631,0.0002060303,0.0001587537,0.00002146844,0.0008823316,0.7757191,0.1799598,0.0003209347,0.0009202814,0.0003785226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8725593,0.0002031616,0.1256366,0.00008141282,0.0007623808,0.0003910847,0.00001255883,0.0001306862,0.000222773],"genre_scores_gemma":[0.9686943,0.0001455683,0.03050115,0.000005612651,0.0005423636,0.00002369733,0.000009108594,0.00004244763,0.00003575428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7132592,"threshold_uncertainty_score":0.7499346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07891345725646022,"score_gpt":0.2950000277181338,"score_spread":0.2160865704616736,"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."}}