{"id":"W2519402035","doi":"10.1002/cpe.3948","title":"Characteristics analysis and optimization design of entities collaboration for cloud manufacturing","year":2016,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Cloud manufacturing; Computer science; Cloud computing; Cluster analysis; Selection (genetic algorithm); Node (physics); Cluster (spacecraft); Process (computing); Preference; Production (economics); Industrial engineering; Artificial intelligence; Computer network","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":[0.001351132,0.0003683536,0.000448682,0.0008779643,0.0006860639,0.001047513,0.0008390673,0.000425553,0.001906566],"category_scores_gemma":[0.004395948,0.000256772,0.000717407,0.0009967757,0.0004085367,0.001123203,0.000768066,0.0003511663,0.0001584102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556174,"about_ca_system_score_gemma":0.001482058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005326516,"about_ca_topic_score_gemma":0.003342476,"domain_scores_codex":[0.9986995,0.0004393519,0.00006644161,0.0002254851,0.0003317462,0.0002373331],"domain_scores_gemma":[0.9974133,0.0009643406,0.0005471848,0.0002638249,0.0006010028,0.0002102915],"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":[0.0001798377,0.00008343426,0.01351616,0.0001025113,0.00007541253,0.0002662066,0.0002059986,0.9083555,0.01037466,0.03354947,0.0008762848,0.03241457],"study_design_scores_gemma":[0.00000784482,0.00004286426,0.002433776,0.000004422815,0.00001992569,0.00005394168,0.00010036,0.990351,0.001908187,0.004291746,0.0007788472,0.000006996244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.515897,0.0002445855,0.4729011,0.0003442146,0.00001797998,0.0001733471,0.0001741381,0.0001930788,0.01005445],"genre_scores_gemma":[0.9751023,0.00006184362,0.02389714,0.00001174256,0.000004300801,0.00005624282,0.00009020663,0.00001511914,0.0007611958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005326516,"threshold_uncertainty_score":0.01129085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160365628008141,"score_gpt":0.2754181111490052,"score_spread":0.2538144548689237,"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."}}