{"id":"W4387016757","doi":"10.32920/24191931","title":"Feature-Based Question Routing in Community Question Answering Platforms","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Interpretability; Question answering; Computer science; Feature (linguistics); Routing (electronic design automation); Information retrieval; Interpretation (philosophy); Rank (graph theory); Temporality; Artificial intelligence; World Wide Web; Computer network; Epistemology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005756834,0.001262172,0.001673205,0.004311719,0.00149833,0.003023255,0.002853023,0.003394402,0.003363912],"category_scores_gemma":[0.02197655,0.0005241163,0.001518878,0.002508991,0.0009277853,0.007711904,0.002915034,0.002621356,0.00154075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001987275,"about_ca_system_score_gemma":0.001627107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009937666,"about_ca_topic_score_gemma":0.009541326,"domain_scores_codex":[0.9952553,0.001892413,0.000308522,0.001483744,0.0006664799,0.0003935837],"domain_scores_gemma":[0.9905493,0.005921894,0.0007923368,0.001118945,0.00112824,0.0004893051],"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":[0.001738091,0.001727491,0.02030139,0.001039368,0.000294229,0.000496974,0.002633258,0.1103288,0.01540627,0.02453619,0.03148115,0.7900168],"study_design_scores_gemma":[0.00009241797,0.0003535689,0.003500147,0.00005713963,0.0000784808,0.0001605187,0.0008132356,0.9273424,0.00616429,0.05360128,0.007775198,0.00006123597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3298495,0.002078263,0.6405655,0.00319451,0.0002598388,0.00101868,0.003917093,0.01331605,0.005800535],"genre_scores_gemma":[0.7163834,0.0002124781,0.2710761,0.0005425824,0.0001714101,0.0003038745,0.007028539,0.0002867003,0.003994996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009937666,"threshold_uncertainty_score":0.03044546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05253523913286398,"score_gpt":0.3135120630124127,"score_spread":0.2609768238795487,"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."}}