{"id":"W2468446116","doi":"10.5281/zenodo.51472","title":"Dataset For Anomaly Detection Using Inter-Arrival Curves For Real-Time Systems","year":2016,"lang":"en","type":"dataset","venue":"INFM-OAR (INFN Catania)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Anomaly (physics); Arrival time; Computer science; Data mining; Real-time computing; Engineering; Physics; Transport engineering","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":[0.0008100283,0.002604559,0.001136129,0.002444579,0.0005442908,0.001029617,0.00208263,0.001663294,0.01098578],"category_scores_gemma":[0.003312945,0.0003761416,0.001180626,0.002940476,0.0003074152,0.0008119336,0.001194588,0.001378102,0.01869375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168678,"about_ca_system_score_gemma":0.001381748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01388604,"about_ca_topic_score_gemma":0.02227112,"domain_scores_codex":[0.9988908,0.0001369087,0.0001868495,0.0002390439,0.0003992633,0.0001471068],"domain_scores_gemma":[0.9983175,0.0002760367,0.0001487813,0.0005595,0.0005646695,0.0001335654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003763609,0.0002222718,0.003676118,0.001044794,0.0001072378,0.0001341494,0.00003785862,0.005185691,0.001399196,0.0005771773,0.9669216,0.02031766],"study_design_scores_gemma":[0.000751343,0.0002976374,0.03173707,0.0004124416,0.0001512937,0.0008554779,0.0002447662,0.02753935,0.008719702,0.003791781,0.9253113,0.0001878175],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00351937,0.0002178012,0.001591793,0.0001582516,0.0001572027,0.00009255475,0.988973,0.003694836,0.001595251],"genre_scores_gemma":[0.003949141,0.00008235761,0.001379585,0.0000362936,0.00001697786,0.0001057844,0.9936918,0.00007872916,0.0006594176],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01388604,"threshold_uncertainty_score":0.03675103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03234106427838934,"score_gpt":0.3097906180519057,"score_spread":0.2774495537735163,"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."}}