{"id":"W4205362389","doi":"10.1109/msec.2020.2988374","title":"Table of Contents","year":2020,"lang":"en","type":"article","venue":"IEEE Security & Privacy","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Niagara","funders":"","keywords":"Table (database); Computer science; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000918378,0.00007374715,0.0001294166,0.00002573994,0.00004534203,0.00005462891,0.000730822,0.0000428683,0.000018203],"category_scores_gemma":[0.00009584178,0.00007404522,0.00004997641,0.0003208912,0.00002106941,0.0003855652,0.0001408635,0.0001180134,0.00006031434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008649827,"about_ca_system_score_gemma":0.00002587595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001133964,"about_ca_topic_score_gemma":0.000003423726,"domain_scores_codex":[0.9992467,0.00003497851,0.0001568069,0.0002218827,0.0001976968,0.0001419625],"domain_scores_gemma":[0.9994093,0.00003185771,0.0000779913,0.0003171228,0.00006800589,0.00009573886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002999974,0.001162981,0.02711447,0.000804988,0.0003640275,0.0001409328,0.1244898,0.0004139707,0.4977591,0.08474588,0.1725017,0.0902022],"study_design_scores_gemma":[0.001591974,0.0006088569,0.004423291,0.00006428748,0.00002170947,0.00001645928,0.00006272044,0.06457134,0.7848237,0.02687754,0.1164167,0.0005213806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7978766,0.0002377891,0.1921255,0.005228286,0.0013036,0.0002108315,0.000007232931,0.0003197081,0.002690457],"genre_scores_gemma":[0.9976013,0.00001105884,0.001509046,0.0007367695,0.0001147671,0.000002289543,6.859866e-7,0.000004652756,0.00001940437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2870646,"threshold_uncertainty_score":0.3019477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03422416541100035,"score_gpt":0.2451721108215227,"score_spread":0.2109479454105224,"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."}}