{"id":"W4239787724","doi":"10.31224/osf.io/b4ckf","title":"Supporting Tools for Transition towards Industry 4.0: A Pressurized Cylinder Manufacturing Case Study","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Scrap; Cylinder; Identification (biology); Work (physics); Welding; Manufacturing; Manufacturing engineering; Business; Engineering; Mechanical engineering; Marketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002349851,0.0003891673,0.0002031479,0.0007984068,0.001015238,0.001859875,0.001313077,0.002104878,0.002577886],"category_scores_gemma":[0.003111735,0.0002627907,0.0004296199,0.0006717283,0.0009078878,0.0018351,0.001538178,0.0008692216,0.000400433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001003832,"about_ca_system_score_gemma":0.001136048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002915716,"about_ca_topic_score_gemma":0.004464451,"domain_scores_codex":[0.9981619,0.000931469,0.00010094,0.0001148115,0.0004615065,0.0002294639],"domain_scores_gemma":[0.9975564,0.001443883,0.0001808604,0.0002691422,0.000341767,0.000207902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002344715,0.007137704,0.04532494,0.003469778,0.0001457577,0.05485017,0.07262581,0.1106863,0.1610049,0.09163342,0.007982905,0.4427937],"study_design_scores_gemma":[0.0006995362,0.006881267,0.0403428,0.001602268,0.000271157,0.01179785,0.1122363,0.3145663,0.2320491,0.02254364,0.256618,0.0003919224],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9318684,0.0001919081,0.05048623,0.0006849794,0.0000282375,0.0005815613,0.0001351489,0.0003321955,0.01569127],"genre_scores_gemma":[0.9147407,0.0002144649,0.07864413,0.00008858321,0.000007256231,0.0002056922,0.000155566,0.00004840641,0.005895061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002915716,"threshold_uncertainty_score":0.01242739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08037616013790226,"score_gpt":0.319091282275848,"score_spread":0.2387151221379458,"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."}}