{"id":"W4386527644","doi":"10.1080/00207543.2023.2254854","title":"A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling","year":2023,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Research Council Canada; Mitacs; McGill University","keywords":"Computer science; Machine learning; Process (computing); Artificial intelligence; Knowledge transfer; Data pre-processing; Data mining; Production (economics); Knowledge extraction; Scarcity; Exploit; Product (mathematics); Preprocessor; Feature selection; Feature (linguistics); Industrial engineering; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004140866,0.001324596,0.001149273,0.002564992,0.0006131574,0.001904117,0.002288514,0.001255499,0.002171714],"category_scores_gemma":[0.006457243,0.0008363294,0.002321323,0.002232568,0.001048736,0.003109988,0.00251364,0.001770714,0.0006464774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001146458,"about_ca_system_score_gemma":0.001936139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007824506,"about_ca_topic_score_gemma":0.005795055,"domain_scores_codex":[0.998336,0.0005243317,0.0001651966,0.000436557,0.0004210589,0.0001169304],"domain_scores_gemma":[0.9963787,0.001985034,0.0003922531,0.000420516,0.0007252279,0.00009831807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001057995,0.0001942095,0.002424342,0.0001910602,0.0002212897,0.0002693305,0.0001637734,0.8475896,0.002639855,0.01899118,0.0007799626,0.1264296],"study_design_scores_gemma":[0.000003905913,0.00004405283,0.0002267123,0.000008255343,0.00001906339,0.00003086157,0.00001054122,0.9897391,0.0007764961,0.008498277,0.0006344697,0.000008308814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005260624,0.0001507132,0.9933074,0.00007918308,0.00001031281,0.00006462186,0.00008781686,0.0004793079,0.0005600471],"genre_scores_gemma":[0.422815,0.0005187211,0.5723291,0.0001450077,0.00006674722,0.0005268345,0.001121678,0.0001566012,0.002320363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007824506,"threshold_uncertainty_score":0.02189928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1596432452131293,"score_gpt":0.4563554472306124,"score_spread":0.2967122020174832,"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."}}