{"id":"W4234232346","doi":"10.1109/grc.2007.4403192","title":"Naïve Bayes Text Classifier","year":2007,"lang":"en","type":"article","venue":"2007 IEEE International Conference on Granular Computing (GRC 2007)","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Naive Bayes classifier; Bayes' theorem; Computer science; Artificial intelligence; Support vector machine; Machine learning; Classifier (UML); Detector; Bayes classifier; Bayesian probability; Bayesian programming; Pattern recognition (psychology); Bayes factor","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001767227,0.0004027822,0.0003121246,0.0005353681,0.0003525467,0.0006068065,0.001801211,0.0002421246,0.0005047822],"category_scores_gemma":[0.0001882279,0.0004071214,0.0002108308,0.0004719978,0.0001262147,0.0004835869,0.0001968391,0.0006745356,0.001556743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002406938,"about_ca_system_score_gemma":0.0001244872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001125849,"about_ca_topic_score_gemma":0.00004786587,"domain_scores_codex":[0.9963839,0.0001191735,0.0007240359,0.0008883071,0.001183134,0.0007014159],"domain_scores_gemma":[0.9977118,0.0003507586,0.0003589831,0.0007040293,0.0006173562,0.0002571199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004346717,0.001071649,0.0113147,0.00006665692,0.0003972021,0.0003900632,0.003232427,0.002954426,0.02715957,0.477985,0.06972314,0.4052705],"study_design_scores_gemma":[0.003637268,0.0008155341,0.02913213,0.000891174,0.00005819172,0.0005488208,0.0002782714,0.5951199,0.02046049,0.02983504,0.3162059,0.003017251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04607819,0.000060499,0.8807139,0.0007476958,0.01180984,0.0002000734,0.000008511659,0.0003915918,0.05998967],"genre_scores_gemma":[0.9696686,0.00002166797,0.02466325,0.001476525,0.001682641,0.000004179552,0.00002176924,0.00003290804,0.002428442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9235904,"threshold_uncertainty_score":0.9998381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06247321536509361,"score_gpt":0.3143555518865284,"score_spread":0.2518823365214348,"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."}}