{"id":"W3080725479","doi":"10.1109/tase.2020.3014907","title":"Interval-Valued Intuitionistic Uncertain Linguistic Cloud Petri Net and Its Application to Risk Assessment for Subway Fire Accident","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China","keywords":"Petri net; Cloud computing; Computer science; Interval (graph theory); Mathematical proof; Artificial intelligence; Data mining; Algorithm; Mathematics","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":[0.0009599316,0.0007033009,0.0004870667,0.001130641,0.0006626036,0.001174329,0.0009979579,0.0004456146,0.001346022],"category_scores_gemma":[0.002119147,0.000264994,0.001383458,0.0007509915,0.0008306163,0.001787665,0.0009026917,0.000901592,0.000127511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00184507,"about_ca_system_score_gemma":0.001649331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01093459,"about_ca_topic_score_gemma":0.006899508,"domain_scores_codex":[0.999252,0.0001457886,0.00005500979,0.0001709105,0.0003003337,0.00007588485],"domain_scores_gemma":[0.9993522,0.000291107,0.0001013695,0.00003895897,0.0001606213,0.00005579626],"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":[0.000195477,0.00007904948,0.003152432,0.0002261606,0.00007613171,0.0006790201,0.0004996737,0.742828,0.008838878,0.1475804,0.001220214,0.09462456],"study_design_scores_gemma":[0.000005058987,0.00001499669,0.0002436789,0.00001168975,0.00001615054,0.00005822084,0.00004013514,0.9812563,0.0014808,0.01608328,0.0007752902,0.00001437761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01284429,0.0001139493,0.9849236,0.0001033176,0.000030406,0.00003759083,0.00003547987,0.0001780757,0.001733359],"genre_scores_gemma":[0.68218,0.0004421375,0.3150039,0.00007011407,0.000041105,0.00008530966,0.000102244,0.00004354179,0.002031774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01093459,"threshold_uncertainty_score":0.02174187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07874959326371368,"score_gpt":0.3919999709221096,"score_spread":0.3132503776583959,"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."}}