{"id":"W3049752840","doi":"10.26874/jumanji.v3i02.59","title":"Analisis Arsitektur E-government dengan Menggunakan Kerangka Kerja Federal Enterprise Architecture (FEA)","year":2019,"lang":"id","type":"article","venue":"JUMANJI (Jurnal Masyarakat Informatika Unjani)","topic":"Information Retrieval and Data Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Humanities; Local government; Geography; Art; Archaeology","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":[0.002444509,0.0002911191,0.0002758266,0.003713418,0.002154394,0.00560355,0.0003804015,0.000656635,0.0132778],"category_scores_gemma":[0.007640587,0.0002302238,0.000484518,0.005999724,0.00110459,0.003613554,0.002020778,0.00105549,0.002689842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003604711,"about_ca_system_score_gemma":0.004162303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03307444,"about_ca_topic_score_gemma":0.04627844,"domain_scores_codex":[0.9959334,0.0008632776,0.0002868054,0.0004615243,0.00175034,0.0007047284],"domain_scores_gemma":[0.9907984,0.003839052,0.0006815487,0.0005859945,0.003595816,0.000499304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003464018,0.0004218384,0.5703337,0.001633693,0.0001709379,0.001502552,0.08710442,0.002074871,0.007271437,0.03948481,0.01651546,0.27314],"study_design_scores_gemma":[0.000008585249,0.0001422993,0.6113941,0.0005591101,0.0001189503,0.0007314177,0.2323908,0.001575447,0.004270478,0.002820088,0.1459129,0.00007578952],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8932868,0.0008646022,0.002127131,0.001222022,0.00003951412,0.0001551729,0.001769665,0.000148798,0.1003864],"genre_scores_gemma":[0.9783648,0.0005973341,0.001717816,0.0001280556,0.000006578908,0.00007070551,0.001260306,0.00004424575,0.01781023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03307444,"threshold_uncertainty_score":0.06576383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007512927062307998,"score_gpt":0.2118485758617029,"score_spread":0.2043356487993949,"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."}}