{"id":"W2106411868","doi":"","title":"Finding Expert Users in Community Question Answering Services Using Topic Models","year":2012,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Latent Dirichlet allocation; Question answering; Topic model; Information retrieval; Set (abstract data type); Matching (statistics); Data science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.008188735,0.001293332,0.001812347,0.0049266,0.001721144,0.004247206,0.002411699,0.00349034,0.002652501],"category_scores_gemma":[0.02373031,0.0009863117,0.001869687,0.002875029,0.0007379868,0.007601968,0.002840397,0.002120808,0.002714587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001923089,"about_ca_system_score_gemma":0.002155568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01902905,"about_ca_topic_score_gemma":0.02178961,"domain_scores_codex":[0.9944707,0.0026128,0.0002659223,0.001528031,0.0007360764,0.0003864356],"domain_scores_gemma":[0.9882825,0.007950051,0.0007745408,0.001069843,0.001230425,0.0006926471],"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":[0.002889108,0.002994569,0.08445445,0.001027965,0.000618539,0.001012198,0.00998979,0.1454114,0.02224488,0.02750656,0.04445838,0.6573921],"study_design_scores_gemma":[0.00007322688,0.0001477588,0.003288782,0.00003409187,0.00007304554,0.0001723526,0.001225592,0.9691741,0.003414604,0.01672843,0.005612929,0.0000550996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3277211,0.001413228,0.6477093,0.002808064,0.00009775091,0.001015578,0.001814007,0.01042125,0.006999651],"genre_scores_gemma":[0.7039632,0.0004305875,0.2848864,0.0004754623,0.0001625363,0.0003374833,0.003843682,0.0004249587,0.005475693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01902905,"threshold_uncertainty_score":0.04330671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426820994540965,"score_gpt":0.1907293881036821,"score_spread":0.1764611781582725,"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."}}