{"id":"W4415367568","doi":"10.1109/icimia67127.2025.11200618","title":"Scam Guard: Intelligent Scam Protection for Users","year":2025,"lang":"","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Guard (computer science); Android (operating system); Communication source; Phishing; User agent; Key (lock); Service provider; Spamming","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":[0.0007364215,0.00160736,0.0008367359,0.001547459,0.0009101349,0.001553119,0.001346738,0.001287661,0.007268405],"category_scores_gemma":[0.005118409,0.0005611789,0.0004665367,0.0002939866,0.0008705997,0.002262036,0.002493449,0.001230447,0.005496852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004090438,"about_ca_system_score_gemma":0.0008158206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00189555,"about_ca_topic_score_gemma":0.001594393,"domain_scores_codex":[0.998704,0.000151322,0.00008554946,0.0002108077,0.0006559826,0.0001923158],"domain_scores_gemma":[0.9961814,0.0006416063,0.0005336073,0.001370082,0.0009473351,0.0003260927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002241379,0.0004408493,0.02570781,0.0009490999,0.0001812179,0.00218703,0.003490748,0.002690468,0.1520328,0.01106555,0.1160581,0.6829549],"study_design_scores_gemma":[0.0003924946,0.002641593,0.0472485,0.0009083972,0.0007597561,0.008728584,0.001160069,0.1946102,0.3044038,0.02033838,0.4181207,0.0006876883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1665201,0.003764739,0.4239639,0.001673518,0.0007090256,0.001611338,0.00115459,0.3316832,0.06891951],"genre_scores_gemma":[0.8974968,0.0008760421,0.05633517,0.001267806,0.0002827859,0.0003901411,0.001206573,0.003543748,0.0386009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007268405,"threshold_uncertainty_score":0.02431524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0305807314304472,"score_gpt":0.2782977814469624,"score_spread":0.2477170500165152,"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."}}