{"id":"W3157372210","doi":"10.1155/2021/5595065","title":"A Monitoring Approach Based on Fuzzy Stochastic P-Timed Petri Nets of a Railway Transport Network","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Petri net; Train; Stochastic Petri net; Computer science; Context (archaeology); Fuzzy logic; Stability (learning theory); Distributed computing; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000222725,0.0001804206,0.0004160385,0.0001432391,0.00004074544,0.000007712277,0.0001067495,0.00008415976,0.000009847757],"category_scores_gemma":[0.00001876347,0.0001731594,0.0002108793,0.0005125939,0.00001773318,0.0001944941,4.681705e-7,0.0002368445,6.210128e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005242922,"about_ca_system_score_gemma":0.00008105765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003068546,"about_ca_topic_score_gemma":0.000009404996,"domain_scores_codex":[0.9983392,0.00002161248,0.0008190706,0.0001398622,0.0004433497,0.0002369163],"domain_scores_gemma":[0.9991402,0.00006719827,0.0002561246,0.0001538389,0.0002781069,0.0001045072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001044539,0.00008702029,0.0005577806,0.0001400364,0.00004674723,0.00004999992,0.0005655929,0.9871469,0.008016895,0.00006066789,0.00001249099,0.00321146],"study_design_scores_gemma":[0.01445615,0.001808263,0.6619472,0.005923426,0.0007405165,0.0001472808,0.003893257,0.2635486,0.04290342,0.0005562128,0.00221732,0.001858337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7495093,0.001284678,0.2467974,0.00002400506,0.001403506,0.0001162833,0.0000096927,0.00004944393,0.0008055998],"genre_scores_gemma":[0.9836162,0.0000677245,0.01595934,0.000009106437,0.0002606211,0.000006881453,0.00001916377,0.00003711508,0.00002387937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7235982,"threshold_uncertainty_score":0.7061235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008148063338936145,"score_gpt":0.2093982140217076,"score_spread":0.2012501506827714,"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."}}