{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000405077,0.0001192069,0.0001599426,0.00003393321,0.0002166368,0.00003953087,0.0003671624,0.00002315395,0.000001286509],"category_scores_gemma":[0.000001397955,0.0001191805,0.00001413626,0.00009498934,0.00001894,0.001492405,0.0002197456,0.0001423969,8.554133e-10],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001820337,"about_ca_system_score_gemma":0.0002943941,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04275119,"about_ca_topic_score_gemma":0.02511519,"domain_scores_codex":[0.9985789,0.0001592536,0.000207539,0.0001245777,0.0006343132,0.0002954091],"domain_scores_gemma":[0.9993537,0.0001310583,0.00009107339,0.0002368898,3.368906e-7,0.0001869075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008998316,0.0001399397,0.4875495,0.0007017832,0.00007140986,0.0000516905,0.0221692,0.007348001,0.08714679,0.3813408,0.0001215378,0.01326936],"study_design_scores_gemma":[0.0007603775,0.00008287228,0.09514159,0.001487901,0.000008413401,0.00003973406,0.02220151,0.7560235,0.1163281,0.002806897,0.004231763,0.0008874091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726164,0.0005903824,0.006210778,0.0006853995,0.0004117776,0.00009299011,0.000007302552,0.00002004757,0.01936496],"genre_scores_gemma":[0.9937706,0.0000345191,0.005365557,0.0005646298,0.00004556377,0.000003268274,0.000001134631,0.000007605729,0.0002071468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7486755,"threshold_uncertainty_score":0.9926739,"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."}}