{"id":"W2477122767","doi":"10.1109/bracis.2016.071","title":"Authorship Attribution via Network Motifs Identification","year":2016,"lang":"en","type":"article","venue":"","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Automatic summarization; Authorship attribution; Identification (biology); Attribution; Function (biology); Style (visual arts); Motif (music)","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":[0.001846663,0.0005661376,0.000630223,0.007977158,0.0006940058,0.00181604,0.0006608413,0.0008823797,0.002260241],"category_scores_gemma":[0.01841636,0.0002824889,0.0004584639,0.00398671,0.0005774312,0.002886261,0.001257942,0.000731305,0.0007203665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007755033,"about_ca_system_score_gemma":0.0005347604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001267865,"about_ca_topic_score_gemma":0.00167864,"domain_scores_codex":[0.998171,0.0006915581,0.0001406433,0.0004416746,0.0003956232,0.0001595412],"domain_scores_gemma":[0.9890191,0.006582763,0.002150041,0.0008162138,0.001162688,0.0002691018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006894122,0.0002389841,0.1316649,0.0005700047,0.000323743,0.0006373011,0.001617973,0.07704359,0.01909142,0.03884124,0.006124906,0.7231566],"study_design_scores_gemma":[0.00002638003,0.00009081372,0.02880382,0.0001151221,0.00009798229,0.0007446995,0.000580662,0.8642119,0.01180277,0.08654211,0.00691878,0.0000648827],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4308226,0.001168266,0.5549173,0.0008363301,0.00011754,0.0002169833,0.002077565,0.001773908,0.008069433],"genre_scores_gemma":[0.9107173,0.0003180101,0.08585744,0.00003339425,0.00007096989,0.0001134286,0.001020014,0.00006152642,0.001807966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007977158,"threshold_uncertainty_score":0.009766161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02819452153527435,"score_gpt":0.2619039526048588,"score_spread":0.2337094310695844,"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."}}