{"id":"W4293730684","doi":"10.3390/machines10090742","title":"Smart Manufacturing—Theories, Methods, and Applications","year":2022,"lang":"en","type":"article","venue":"Machines","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Measure (data warehouse); Smart manufacturing; Computer science; Manufacturing engineering; Engineering; Data mining","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":[0.0001110873,0.00006914626,0.00006164757,0.00004681472,0.0001233358,0.00003451642,0.00008738141,0.00001687408,0.0002716022],"category_scores_gemma":[0.000002721453,0.00007212387,0.00001559326,0.00007198312,0.00001894654,0.0001000895,0.00003841761,0.000143434,0.00001069694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001970935,"about_ca_system_score_gemma":0.000003106316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005993335,"about_ca_topic_score_gemma":0.000001236407,"domain_scores_codex":[0.9996573,0.0000180375,0.0001017588,0.00006980509,0.00006589907,0.00008723752],"domain_scores_gemma":[0.9997963,0.00004682373,0.000008841933,0.0001117344,0.000003852827,0.00003248009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005908133,0.00003124198,0.001504158,0.0001791519,0.00006433048,0.000002066156,0.0005470252,0.02982045,0.0005072214,0.07933131,0.003731687,0.8842754],"study_design_scores_gemma":[0.0001234605,0.000008790721,0.001125371,0.000001731964,0.000007510477,0.00003134615,0.0001644283,0.003110172,0.003183522,0.02177951,0.970307,0.0001571654],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06164623,0.001388859,0.2906542,0.0004488995,0.0009370196,0.0006938028,0.0003295418,0.001746223,0.6421553],"genre_scores_gemma":[0.9913108,0.00001808875,0.006832205,0.0001011029,0.0000803273,0.0004583532,0.00005127919,0.00003087175,0.001116958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9665753,"threshold_uncertainty_score":0.2973853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014435829958817,"score_gpt":0.2609161380977357,"score_spread":0.2507717797981476,"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."}}