{"id":"W3113990471","doi":"10.3390/info12010002","title":"The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study","year":2020,"lang":"en","type":"article","venue":"Information","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Malware; Computer science; The Internet; Cluster (spacecraft); Phishing; Resource (disambiguation); Computer security; Population; World Wide Web; Demography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001186079,0.0004661359,0.0004274001,0.004492232,0.000681922,0.000938415,0.0009028861,0.0008096482,0.001016433],"category_scores_gemma":[0.004574327,0.0001391105,0.0005733157,0.005554672,0.0005060307,0.0008794286,0.001266902,0.0004241519,0.0007420282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531938,"about_ca_system_score_gemma":0.001354191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07311129,"about_ca_topic_score_gemma":0.08885406,"domain_scores_codex":[0.9986719,0.0003314268,0.0001554807,0.0002594105,0.0003574885,0.0002242621],"domain_scores_gemma":[0.9975351,0.0005705797,0.000501084,0.000419924,0.0007842389,0.0001890769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004523088,0.0003895291,0.8071944,0.001126272,0.0002425344,0.003099403,0.00221008,0.02548981,0.003378286,0.005199646,0.08124436,0.06997333],"study_design_scores_gemma":[0.00007420811,0.0003018832,0.7326227,0.0004663889,0.0002078953,0.005058525,0.01207148,0.07891582,0.007955517,0.002988002,0.1591809,0.0001566256],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7906071,0.001512261,0.008264557,0.002317515,0.0001700961,0.0004174891,0.1865696,0.0007228894,0.00941841],"genre_scores_gemma":[0.810816,0.0007538347,0.01540645,0.000220501,0.0001036826,0.000272268,0.1693058,0.00008041754,0.003041041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07311129,"threshold_uncertainty_score":0.1453715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324729539169537,"score_gpt":0.2677901795738267,"score_spread":0.2545428841821313,"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."}}