{"id":"W2000389083","doi":"10.1145/2187980.2188202","title":"Finding expert users in community question answering","year":2012,"lang":"en","type":"article","venue":"","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Question answering; Topic model; Information retrieval; Matching (statistics); Set (abstract data type); Focus (optics); Data science; 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":[0.001040153,0.00008392713,0.0001031681,0.0001196446,0.000158599,0.00007743996,0.0004435676,0.00005491801,0.000007055163],"category_scores_gemma":[0.0000665985,0.00007513771,0.00002558943,0.000243486,0.00001303172,0.0008182156,0.0001527091,0.0001935559,0.00004743167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009313714,"about_ca_system_score_gemma":0.00001355199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002561106,"about_ca_topic_score_gemma":0.0001216697,"domain_scores_codex":[0.9990655,0.0002469137,0.0001580957,0.00009977633,0.0001270423,0.0003027256],"domain_scores_gemma":[0.9993512,0.0001034811,0.00003389598,0.0003930997,0.00001218714,0.0001061196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007290702,0.0005294533,0.5996967,0.00005750264,0.00001987101,0.000009399441,0.1629859,0.0001964088,0.03084273,0.165314,0.007063791,0.03327692],"study_design_scores_gemma":[0.003855435,0.0005740802,0.5279635,0.002114827,0.000008205409,0.0003581916,0.02974943,0.183029,0.1411707,0.002262302,0.104813,0.004101255],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7957714,0.0002871921,0.1897051,0.0004311833,0.001109795,0.00009529029,1.580854e-7,0.0003141738,0.01228567],"genre_scores_gemma":[0.9876041,0.000007504531,0.01177116,0.0002553235,0.00007981007,0.00001121669,7.666368e-7,0.000005403647,0.0002647064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1918327,"threshold_uncertainty_score":0.3871643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05150179347618641,"score_gpt":0.3147266579913301,"score_spread":0.2632248645151437,"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."}}