{"id":"W1988374254","doi":"10.1145/2700481","title":"Identifying Authorities in Online Communities","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dependency (UML); Identification (biology); Set (abstract data type); Feature vector; Function (biology); Feature (linguistics); Artificial intelligence; Online community; Machine learning; Reading (process); Data mining; Information retrieval; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003863865,0.001068344,0.001557941,0.01160015,0.002587595,0.002756689,0.001929314,0.003309611,0.00232558],"category_scores_gemma":[0.01615607,0.0008251899,0.001563704,0.005769813,0.00271991,0.007786306,0.004350415,0.001582716,0.001168828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001689219,"about_ca_system_score_gemma":0.001183804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006167583,"about_ca_topic_score_gemma":0.005760965,"domain_scores_codex":[0.9955113,0.001700914,0.0002606398,0.001221705,0.0009271908,0.0003783404],"domain_scores_gemma":[0.9906592,0.004075859,0.002117215,0.001155741,0.001368683,0.0006231777],"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":[0.001096467,0.0008086803,0.1645443,0.001379765,0.0005377489,0.001839688,0.0105588,0.1467714,0.01653315,0.2242262,0.01634675,0.415357],"study_design_scores_gemma":[0.00006163858,0.0001520803,0.02312035,0.0001556117,0.0001462396,0.00117094,0.003193076,0.7996323,0.005360065,0.1504486,0.01638684,0.0001721926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2673018,0.003330238,0.7161132,0.001398334,0.0001089784,0.0005773812,0.001053478,0.001807581,0.00830893],"genre_scores_gemma":[0.8704971,0.0006443748,0.1240539,0.0001164889,0.0001634963,0.0001800608,0.001085209,0.0001002129,0.003159083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01160015,"threshold_uncertainty_score":0.02043432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09771654972236936,"score_gpt":0.3155047130386311,"score_spread":0.2177881633162618,"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."}}