{"id":"W4254325038","doi":"10.1145/1357010.1352620","title":"Itrustpage","year":2008,"lang":"en","type":"article","venue":"ACM SIGOPS Operating Systems Review","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Phishing; Computer science; False positive paradox; Automation; World Wide Web; Computer security; False positives and false negatives; Internet privacy; The Internet; Artificial intelligence; Engineering","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":[0.002686405,0.002303204,0.001174441,0.002893889,0.001269076,0.00389216,0.003160924,0.002247028,0.03643424],"category_scores_gemma":[0.01659523,0.00188451,0.001670961,0.002563559,0.001230333,0.008402419,0.004861755,0.004004952,0.04496051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007437632,"about_ca_system_score_gemma":0.00147786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001920614,"about_ca_topic_score_gemma":0.001368945,"domain_scores_codex":[0.9959085,0.0004919454,0.0003379284,0.0008260469,0.001878758,0.0005568537],"domain_scores_gemma":[0.987174,0.003046712,0.001415331,0.004899352,0.002606214,0.0008582532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001827863,0.0004674171,0.01357063,0.001333937,0.0002177301,0.001858233,0.002266871,0.00205969,0.01199292,0.01488225,0.6816058,0.2679167],"study_design_scores_gemma":[0.0002384777,0.0007787599,0.01015768,0.0003910876,0.0001727307,0.003659282,0.0002789273,0.02226966,0.0335117,0.01108922,0.9171123,0.0003402135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.02451343,0.00185286,0.09125485,0.001059765,0.001238656,0.0007744257,0.01028459,0.7850943,0.08392719],"genre_scores_gemma":[0.3704567,0.004234847,0.1285174,0.00431782,0.001618967,0.001490276,0.07938118,0.2352812,0.1747016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03643424,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04596051882530987,"score_gpt":0.26949806565528,"score_spread":0.2235375468299701,"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."}}