{"id":"W3199117479","doi":"10.3233/faia210012","title":"Extreme Gradient Boosting for Cyberpropaganda Detection","year":2021,"lang":"en","type":"article","venue":"Frontiers in artificial intelligence and applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Boosting (machine learning); Computer science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002071858,0.00008470059,0.0001093197,0.0001001925,0.0003053116,0.0001303225,0.0001711673,0.00006038868,0.000004197853],"category_scores_gemma":[0.00004613624,0.00009335049,0.00004141921,0.0006109381,0.00004971729,0.0002203448,0.00006616148,0.0001111348,0.000006380775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004104187,"about_ca_system_score_gemma":0.00003170944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001591118,"about_ca_topic_score_gemma":0.0001593014,"domain_scores_codex":[0.9990504,0.00002840552,0.0002680499,0.0003719653,0.0000872422,0.0001939521],"domain_scores_gemma":[0.9994961,0.00005837802,0.00006050476,0.0002330565,0.00009572995,0.00005623099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000006211354,0.00005117492,0.00005729413,0.000008440422,0.000003647479,9.712614e-7,0.0002176977,0.0002623623,0.002034214,0.07695296,0.0001127278,0.9202923],"study_design_scores_gemma":[0.00003715366,0.00005284639,0.00009128368,0.00002326079,0.000007061427,0.00001392562,0.0004711759,0.3806663,0.1541566,0.4240426,0.04024298,0.0001947884],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003335673,0.0005816437,0.9944451,0.0004669001,0.0005025802,0.0003442809,0.000002375418,0.00006247214,0.0002590147],"genre_scores_gemma":[0.898966,0.0003680833,0.09955768,0.0001990341,0.000288749,0.0005106418,0.000007179921,0.000009030799,0.00009356767],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9200975,"threshold_uncertainty_score":0.3806722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213326009959081,"score_gpt":0.2629436923944674,"score_spread":0.2208104322948765,"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."}}