{"id":"W2098769355","doi":"10.4108/icst.collaboratecom2009.8310","title":"CASTLE: A social framework for collaborative anti-phishing databases","year":2009,"lang":"en","type":"article","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Phishing; Cornerstone; Computer science; The Internet; World Wide Web; Internet users; Computer security; Internet privacy; Database; History","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":[0.007312411,0.000882136,0.001366912,0.003570008,0.004083931,0.006675767,0.004878914,0.003372217,0.006758798],"category_scores_gemma":[0.01566357,0.001092763,0.001515941,0.002775426,0.003051926,0.009939843,0.01028601,0.003193193,0.002404568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148015,"about_ca_system_score_gemma":0.003233542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006744725,"about_ca_topic_score_gemma":0.009184117,"domain_scores_codex":[0.9942577,0.002130509,0.0004394976,0.0008849779,0.001863757,0.0004234649],"domain_scores_gemma":[0.988021,0.003810446,0.000984433,0.004470128,0.001069374,0.001644736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003844915,0.0005991401,0.003339964,0.0004500437,0.0002190039,0.0007747079,0.002116631,0.05743259,0.006463843,0.7252312,0.02932621,0.1736623],"study_design_scores_gemma":[0.0002000074,0.0001988478,0.0008817886,0.0001219098,0.0001112338,0.0006202681,0.0005676821,0.4869558,0.004371265,0.3343436,0.1714875,0.0001401826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007341544,0.0004307684,0.9757234,0.00153038,0.0001487412,0.0004591206,0.0004519907,0.006909649,0.007004505],"genre_scores_gemma":[0.2412225,0.000865053,0.7394381,0.0005237625,0.0003927921,0.0008740142,0.001552802,0.0007931539,0.01433784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007312411,"threshold_uncertainty_score":0.03867221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05825021323630836,"score_gpt":0.3282145563115197,"score_spread":0.2699643430752113,"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."}}