{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004073514,0.0009641079,0.002108043,0.001103854,0.000560583,0.001200153,0.001447295,0.001587355,0.001461827],"category_scores_gemma":[0.007078338,0.0004863013,0.0008200833,0.0009928728,0.0009565412,0.001132861,0.00114317,0.001749805,0.0009374481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005898749,"about_ca_system_score_gemma":0.0009098276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0015725,"about_ca_topic_score_gemma":0.0009973139,"domain_scores_codex":[0.9985719,0.0007465328,0.00005851586,0.0001795363,0.0002968667,0.0001465745],"domain_scores_gemma":[0.9972565,0.001726892,0.0001945147,0.0002194542,0.0004921022,0.0001107027],"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":[0.000318964,0.0002269761,0.003208376,0.0001921323,0.0001452629,0.0001955707,0.0001105524,0.6876475,0.004718428,0.0147899,0.008548949,0.2798974],"study_design_scores_gemma":[0.000004435215,0.00001941264,0.0001696596,0.000005375027,0.000005576456,0.00001790135,0.00000363447,0.9949471,0.0004793024,0.003909908,0.0004339099,0.000003712217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02706687,0.0009808998,0.9686888,0.0004600345,0.000129117,0.0000552688,0.00007794618,0.0009314024,0.001609656],"genre_scores_gemma":[0.736339,0.0006965937,0.255577,0.0005460417,0.0003627219,0.0001690732,0.0005203263,0.0002326919,0.005556561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004073514,"threshold_uncertainty_score":0.02154309,"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."}}