{"id":"W1489946595","doi":"10.2139/ssrn.488444","title":"Information Asymmetry and Thwarting Spam","year":2004,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Quest University Canada","funders":"","keywords":"Information asymmetry; Business; Computer science; Computer security","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":[0.00289864,0.0002240474,0.0004744003,0.001710897,0.001122993,0.002476784,0.0003933522,0.002053425,0.005366632],"category_scores_gemma":[0.02578249,0.0002516722,0.0002390927,0.001013452,0.001580243,0.004192834,0.001061779,0.001490424,0.0006774647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000804325,"about_ca_system_score_gemma":0.0007137036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008169311,"about_ca_topic_score_gemma":0.0007627444,"domain_scores_codex":[0.9988092,0.0005023127,0.00004434775,0.00008058143,0.0003145821,0.0002490177],"domain_scores_gemma":[0.9766726,0.01394752,0.004595984,0.002150145,0.001798877,0.0008348948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002393493,0.0005618056,0.09582646,0.0003090586,0.0001459581,0.001657102,0.003096569,0.01379097,0.008120595,0.6298172,0.01743581,0.226845],"study_design_scores_gemma":[0.0001115584,0.0002901212,0.03533707,0.0000939219,0.0001057267,0.001468165,0.001919057,0.0454856,0.00489321,0.8968094,0.01344112,0.00004497217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8881512,0.001934604,0.02363747,0.009680705,0.0001728375,0.00005602278,0.0001878522,0.0001458869,0.07603345],"genre_scores_gemma":[0.9974363,0.0002561522,0.0005326926,0.0001765998,0.0001013893,0.000005621989,0.00001349553,0.000005196169,0.001472534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005366632,"threshold_uncertainty_score":0.01795316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003781619304591854,"score_gpt":0.1970646490945143,"score_spread":0.1932830297899225,"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."}}