{"id":"W1409627487","doi":"","title":"Detection and Filtering Spam using Feature Selection and Learning Machine Methods","year":2015,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Feature selection; Variety (cybernetics); Decision tree; Machine learning; Data mining; Popularity; Selection (genetic algorithm); Artificial intelligence; Offensive; Tree (set theory); Feature extraction; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001069312,0.0000933341,0.0001966061,0.0001107835,0.000248128,0.00005750664,0.00005779238,0.00008945598,8.895361e-8],"category_scores_gemma":[0.0001345291,0.00007335353,0.00001478334,0.0001489324,0.0000298845,0.0003077412,0.000116735,0.000728615,6.005813e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002948025,"about_ca_system_score_gemma":0.00001092001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005209187,"about_ca_topic_score_gemma":0.000001474885,"domain_scores_codex":[0.9994025,0.00008293571,0.0001655338,0.0001307199,0.0001174391,0.0001008753],"domain_scores_gemma":[0.9995189,0.0001102572,0.0002136754,0.00002802754,0.00005546326,0.00007364908],"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":[0.0001392365,0.000007497596,0.003847013,0.00007665789,0.0001831441,0.000003327018,0.01056697,0.00170938,0.4229349,0.0003762271,0.0001042862,0.5600514],"study_design_scores_gemma":[0.006452858,0.002566477,0.02928204,0.0007049037,0.000639235,0.01040249,0.01732541,0.5918484,0.2847021,0.03262699,0.02188778,0.001561298],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6347435,0.01028064,0.3544066,0.0002585894,0.0002157502,0.00003764915,6.604906e-8,0.00002190971,0.00003531595],"genre_scores_gemma":[0.9199159,0.00165095,0.07814188,0.00004847489,0.0002246059,8.03394e-7,2.384645e-8,0.000004684977,0.0000126741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.590139,"threshold_uncertainty_score":0.316551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06891504302138124,"score_gpt":0.3569574012045877,"score_spread":0.2880423581832064,"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."}}