{"id":"W2096575814","doi":"10.1109/wi.2006.50","title":"Binary Cybergenre Classification Using Theoretic Feature Measures","year":2006,"lang":"en","type":"article","venue":"","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Feature selection; Computer science; Feature (linguistics); Binary classification; Artificial intelligence; Context (archaeology); Set (abstract data type); Pattern recognition (psychology); Selection (genetic algorithm); Binary number; Feature extraction; Web page; Data mining; Machine learning; Support vector machine; Mathematics; World Wide Web","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":[0.0003550464,0.00009070278,0.00007500985,0.00006765182,0.0001812663,0.0001012962,0.000334931,0.00008821845,0.00003721763],"category_scores_gemma":[0.00002137467,0.00007374235,0.00004699879,0.0003805931,0.00003439308,0.0002455464,0.00006323076,0.0001157096,0.00005648863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000444632,"about_ca_system_score_gemma":0.00004719564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002905902,"about_ca_topic_score_gemma":0.00000505253,"domain_scores_codex":[0.9991434,0.0001021021,0.0001231035,0.0002413146,0.000205205,0.0001848643],"domain_scores_gemma":[0.9994686,0.00003392555,0.00005551964,0.0003159001,0.00008311058,0.00004289384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002307433,0.00001850929,0.001551413,0.000004057355,0.000002667073,0.000003035967,0.00005439482,0.0002427604,0.01842034,0.9752695,0.001839887,0.002591149],"study_design_scores_gemma":[0.0003409291,0.00004395145,0.04779091,0.00003256116,0.00001784973,0.00005372487,0.0001209577,0.8117631,0.0350931,0.08920407,0.01499102,0.0005478211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03013682,0.0002757098,0.9528914,0.002460794,0.0002951436,0.00009208148,0.000001069078,0.0002864737,0.01356049],"genre_scores_gemma":[0.9538179,0.000004034893,0.04440495,0.0001714672,0.00008888858,0.000002692833,0.000007191949,0.000005317168,0.001497562],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9236811,"threshold_uncertainty_score":0.3007126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04985563926123843,"score_gpt":0.2836080435254115,"score_spread":0.2337524042641731,"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."}}