{"id":"W3018303442","doi":"10.1080/00207543.2020.1752488","title":"Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies","year":2020,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":154,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Manufacturing engineering; Engineering; Algorithm; Industrial engineering; Artificial intelligence","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":[0.002312273,0.0005104213,0.000586582,0.001378426,0.0005724958,0.003581577,0.0009476691,0.002243878,0.005945385],"category_scores_gemma":[0.00498391,0.0001998651,0.0005121517,0.003097773,0.001139188,0.001940281,0.0007122403,0.001745527,0.0006649139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008467783,"about_ca_system_score_gemma":0.0006399411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001172762,"about_ca_topic_score_gemma":0.001262928,"domain_scores_codex":[0.9988686,0.0006637211,0.00005274148,0.00007326146,0.0002721827,0.00006947994],"domain_scores_gemma":[0.9973671,0.00225409,0.00006243172,0.00007009125,0.0001994436,0.00004694337],"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":[0.0003156429,0.0007630897,0.008471205,0.001636569,0.0001025279,0.001065765,0.0007015207,0.1741437,0.003751695,0.1364498,0.02023363,0.6523649],"study_design_scores_gemma":[0.00008871289,0.000608177,0.009087631,0.001021709,0.0001162449,0.002302046,0.001898175,0.699842,0.0100244,0.1623902,0.1125414,0.00007925167],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.2782983,0.07727198,0.4430476,0.01939582,0.0006148124,0.0006161619,0.000420125,0.0007240876,0.1796111],"genre_scores_gemma":[0.7546137,0.04726004,0.172933,0.0009574613,0.0003949248,0.0002741652,0.0003848857,0.0001105243,0.02307131],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005945385,"threshold_uncertainty_score":0.0198893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1706827769082056,"score_gpt":0.4110385163572294,"score_spread":0.2403557394490238,"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."}}