{"id":"W2848618769","doi":"","title":"Authorship Identification for Literary Book Recommendations","year":2018,"lang":"en","type":"article","venue":"International Conference on Computational Linguistics","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Reading (process); Recommender system; Pleasure; Computer science; Identification (biology); Style (visual arts); Factor (programming language); Writing style; Qualitative analysis; Information retrieval; World Wide Web; Natural language processing; Artificial intelligence; Qualitative research; Psychology; Linguistics; Literature; Art; Sociology","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":[0.002096687,0.00096813,0.0009009565,0.005797121,0.001433809,0.002134788,0.001305084,0.001524805,0.006038892],"category_scores_gemma":[0.015784,0.0005235341,0.0009393167,0.003172705,0.0002998029,0.003510542,0.001121757,0.001401497,0.007183637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008642376,"about_ca_system_score_gemma":0.001289081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005264567,"about_ca_topic_score_gemma":0.01408992,"domain_scores_codex":[0.9978028,0.0005157184,0.0002075945,0.0007417834,0.0005605106,0.0001716538],"domain_scores_gemma":[0.9917223,0.0033831,0.0007185491,0.001696616,0.002046001,0.000433442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004531674,0.0007295368,0.06016108,0.0005872764,0.000237983,0.0003246317,0.0009987069,0.01095793,0.007996833,0.003237958,0.0381837,0.8761312],"study_design_scores_gemma":[0.00009023163,0.0003132463,0.0373199,0.0003924035,0.0003441477,0.001249736,0.001099382,0.8548715,0.02523815,0.01777762,0.0611385,0.0001651387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2485244,0.007022094,0.6640635,0.003445747,0.001466942,0.001123033,0.01254236,0.03155316,0.03025878],"genre_scores_gemma":[0.6959285,0.001077746,0.2756044,0.0002921087,0.0004518348,0.0002178476,0.008792351,0.0003737731,0.01726159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006038892,"threshold_uncertainty_score":0.02020216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127695465733507,"score_gpt":0.3943209370164236,"score_spread":0.2815513904430729,"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."}}