{"id":"W2123641544","doi":"10.1109/iscc.2009.5202287","title":"Online spam filtering using support vector machines","year":2009,"lang":"en","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Computer science; USable; Preprocessor; Artificial intelligence; Feature (linguistics); Feature vector; Pattern recognition (psychology); String (physics); Data mining; Contrast (vision); Task (project management); Filter (signal processing); Kernel (algebra); Machine learning; Mathematics; Computer vision","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":[0.001835772,0.000657004,0.001531836,0.001769994,0.0005115043,0.001425455,0.0012174,0.00169117,0.001611133],"category_scores_gemma":[0.006758161,0.0003737917,0.0006617413,0.00107792,0.0005518929,0.002706309,0.0008521731,0.001050095,0.001117931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005447257,"about_ca_system_score_gemma":0.0005271384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009619,"about_ca_topic_score_gemma":0.0007604989,"domain_scores_codex":[0.9985586,0.0004490775,0.0001190406,0.0002462877,0.0004842933,0.0001427635],"domain_scores_gemma":[0.9953667,0.002475488,0.0004317733,0.0005955643,0.00100826,0.0001222793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005927653,0.0004084458,0.00238485,0.0001352159,0.0001242854,0.0001988847,0.0001285909,0.1624664,0.01748567,0.01135725,0.004042149,0.8006754],"study_design_scores_gemma":[0.00001098303,0.00003360879,0.0001816751,0.000004161833,0.000008137292,0.00002681628,0.000006406506,0.9916298,0.003068172,0.004556385,0.0004677752,0.00000596241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04078716,0.0004082683,0.9545818,0.0002221222,0.0000675825,0.00003447079,0.00004220205,0.002774049,0.001082402],"genre_scores_gemma":[0.7606967,0.0002639987,0.2353688,0.0001323133,0.0002436159,0.00008147521,0.0002213805,0.0001061322,0.002885609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001835772,"threshold_uncertainty_score":0.009708643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03212964154874209,"score_gpt":0.2787890473127991,"score_spread":0.246659405764057,"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."}}