{"id":"W4328030220","doi":"10.1109/icipnp57450.2022.00041","title":"A System and Method of Production Line Task Allocation Based on MEC","year":2022,"lang":"en","type":"article","venue":"","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Task (project management); Production line; Task analysis; Enhanced Data Rates for GSM Evolution; Genetic algorithm; Distributed computing; Process (computing); Production (economics); Real-time computing; Artificial intelligence; Machine learning; Engineering; Systems engineering; Operating system","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":[0.0004325665,0.0006548735,0.0006700826,0.0009388967,0.0009693247,0.001094695,0.00132082,0.0006922335,0.00352107],"category_scores_gemma":[0.001054763,0.0003079608,0.0005641225,0.001035315,0.0003973655,0.001421944,0.0009892107,0.0005771366,0.0008126282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000818399,"about_ca_system_score_gemma":0.001496814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007021071,"about_ca_topic_score_gemma":0.003968253,"domain_scores_codex":[0.999368,0.00008259017,0.00005577682,0.0002101126,0.0002037023,0.00007986918],"domain_scores_gemma":[0.9997029,0.00004894226,0.00002428846,0.00006405948,0.0001310321,0.00002868986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004234276,0.0002119586,0.003993461,0.0003990539,0.000123009,0.0005862978,0.000480699,0.2886026,0.05668245,0.03295965,0.01066738,0.60487],"study_design_scores_gemma":[0.00007502672,0.0001257307,0.001057202,0.00002911584,0.0000627829,0.000348446,0.0001030994,0.961899,0.01254145,0.007380084,0.01632618,0.00005185365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01487279,0.0002731053,0.974103,0.0001541598,0.00008337936,0.0001740001,0.00008068822,0.002887556,0.007371345],"genre_scores_gemma":[0.5239428,0.0004840015,0.462872,0.0001647511,0.00008169361,0.0005589564,0.0003604622,0.0001749751,0.01136028],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007021071,"threshold_uncertainty_score":0.01396042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01589983896826563,"score_gpt":0.23185212839322,"score_spread":0.2159522894249543,"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."}}