{"id":"W2802083172","doi":"10.1139/tcsme-2013-0084","title":"CLOUD COMPUTING BASED INTELLIGENT MANUFACTURING SCHEDULING SYSTEM USING THE QUALITY PREDICTION METHOD","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cloud computing; Computer science; Scheduling (production processes); Cloud manufacturing; Real-time computing; Production line; Distributed computing; Database; Industrial engineering; Engineering; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005698297,0.0005288007,0.0008475807,0.0007738062,0.0008040775,0.001147315,0.001203746,0.0003844768,0.001266762],"category_scores_gemma":[0.001072849,0.0002345232,0.0004467955,0.001268617,0.0002373894,0.001038716,0.000573991,0.0004358864,0.0004305616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038115,"about_ca_system_score_gemma":0.001425234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01382605,"about_ca_topic_score_gemma":0.007653881,"domain_scores_codex":[0.9993876,0.00008002979,0.00004353162,0.0001443604,0.0002409823,0.0001034594],"domain_scores_gemma":[0.9993837,0.0001102481,0.0001027906,0.0001076953,0.000212385,0.00008309583],"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":[0.001364507,0.0007013637,0.01292477,0.0001820635,0.0002170933,0.000524604,0.0001795518,0.4325196,0.04044036,0.01474011,0.01745049,0.4787554],"study_design_scores_gemma":[0.00003383864,0.00004035785,0.001098316,0.000005449817,0.00002021638,0.00004038276,0.00001444706,0.989957,0.005248834,0.001539889,0.001988689,0.00001251159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1279697,0.0009268737,0.8469312,0.0008055576,0.0002695255,0.0003168762,0.000581052,0.01053813,0.0116611],"genre_scores_gemma":[0.870001,0.0003181258,0.1266612,0.0001312712,0.00008320383,0.0000987224,0.0005912071,0.00008290008,0.002032485],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01382605,"threshold_uncertainty_score":0.02749115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382371141443106,"score_gpt":0.2519168918447014,"score_spread":0.2280931804302703,"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."}}