{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008110788,0.00007534009,0.00007381184,0.00006028014,0.00007468828,0.0001203062,0.000280781,0.00002917821,0.00007820332],"category_scores_gemma":[0.00001360556,0.00006520726,0.00003516674,0.000188016,0.000005444196,0.0003767635,0.0000499938,0.00006684854,0.00002272569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002120901,"about_ca_system_score_gemma":0.00001769716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009032968,"about_ca_topic_score_gemma":0.00001697915,"domain_scores_codex":[0.9994348,0.00001261847,0.0001052926,0.0001845582,0.0001150132,0.0001477602],"domain_scores_gemma":[0.9996466,0.00001277379,0.00002888688,0.0002409367,0.00002153861,0.00004923962],"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":[0.00002386733,0.0005486284,0.00354628,0.00002687509,0.00003207632,0.0001022186,0.001380728,0.002943159,0.360238,0.02629243,0.005053494,0.5998123],"study_design_scores_gemma":[0.0002776764,0.0003068183,0.040216,0.00001906164,0.000006464326,0.0001235507,0.000007992031,0.9264147,0.02262439,0.003556503,0.006108672,0.0003382131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3257044,0.00001969277,0.6690331,0.001049624,0.0007963587,0.00005214922,0.000001262423,0.0003996709,0.002943777],"genre_scores_gemma":[0.9000065,0.000002001837,0.09861755,0.000747826,0.000231556,2.780427e-7,0.000001963064,0.000003485133,0.0003888199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9234715,"threshold_uncertainty_score":0.2659075,"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."}}