{"id":"W4232303630","doi":"10.32920/ryerson.14652015","title":"Spam detection system: a new approach based on interval type-2 fuzzy sets","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Spamming; Computer science; The Internet; Interval (graph theory); Filter (signal processing); Forum spam; Fuzzy logic; Set (abstract data type); Artificial intelligence; Spambot; Data mining; Machine learning; World Wide Web; 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":[0.0007026999,0.0005498556,0.0008332958,0.001523287,0.0005343887,0.001122727,0.001024718,0.0009601763,0.002122738],"category_scores_gemma":[0.00150985,0.0002583629,0.0007607717,0.0007269769,0.0004312079,0.001096919,0.000417012,0.0007323634,0.0007799823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007256711,"about_ca_system_score_gemma":0.0005724331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002256609,"about_ca_topic_score_gemma":0.001498646,"domain_scores_codex":[0.9992423,0.0001301136,0.00006091763,0.0001644806,0.0003673478,0.00003493335],"domain_scores_gemma":[0.9994726,0.0001638813,0.00005968145,0.00003900806,0.0002456708,0.00001924723],"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":[0.0006269153,0.0003284861,0.002979282,0.0005410696,0.0002040727,0.0004311673,0.0005304592,0.07275382,0.07230438,0.01919811,0.007306903,0.8227954],"study_design_scores_gemma":[0.00004816708,0.0002703231,0.001570964,0.00005880048,0.0001038303,0.0002983021,0.00007754162,0.9608722,0.01970968,0.005693342,0.01123571,0.00006120968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01145947,0.0003706287,0.9834303,0.000119426,0.0001330862,0.0001127499,0.00006037246,0.001268515,0.003045511],"genre_scores_gemma":[0.3190622,0.0005917239,0.6736836,0.0002583111,0.0001877154,0.0002840458,0.0001985695,0.0000602672,0.005673509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002256609,"threshold_uncertainty_score":0.007101238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03646633155453138,"score_gpt":0.2513245721095181,"score_spread":0.2148582405549867,"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."}}