{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003643793,0.0001611398,0.0002797034,0.0009390641,0.0001703794,0.0001382474,0.000855692,0.0002170477,0.000002788699],"category_scores_gemma":[0.00002431715,0.0001495617,0.00003361655,0.0005573801,0.0001161063,0.0002152563,0.00004599476,0.0003869863,0.00003275003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009550135,"about_ca_system_score_gemma":0.0000409986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002093791,"about_ca_topic_score_gemma":0.0007156327,"domain_scores_codex":[0.9988146,0.0001029847,0.0003937811,0.0002412814,0.0001793159,0.0002680558],"domain_scores_gemma":[0.9988071,0.0001167135,0.0000730649,0.0008363217,0.00009175407,0.00007507246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004794055,0.001436815,0.01417303,0.00050273,0.0002920492,0.0001781306,0.05220938,0.00360196,0.0006835944,0.6471654,0.001028956,0.27868],"study_design_scores_gemma":[0.003673018,0.003080922,0.0006578171,0.004198075,0.00006570388,0.003002832,0.590194,0.1455445,0.0173844,0.107775,0.1214137,0.003009937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1102381,0.003217727,0.8810836,0.00222603,0.002041435,0.0002822882,0.00001119972,0.0006113385,0.000288258],"genre_scores_gemma":[0.9952534,0.0002629181,0.00364244,0.00002841624,0.00002743454,0.00008258247,0.000002415786,0.00001147331,0.0006889613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8850152,"threshold_uncertainty_score":0.6098949,"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."}}