{"id":"W4387017576","doi":"10.32920/24191931.v1","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; Computer science; Question answering; Feature (linguistics); Interpretation (philosophy); Routing (electronic design automation); Information retrieval; Temporality; Rank (graph theory); World Wide Web; Artificial intelligence; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003415386,0.0003217412,0.0003960372,0.0004481276,0.0002761577,0.0004523721,0.001371579,0.0005192996,0.00000119601],"category_scores_gemma":[0.0002285696,0.000294255,0.0001156127,0.0004483822,0.00002747747,0.0002988726,0.001182651,0.00199882,0.00005063748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004384098,"about_ca_system_score_gemma":0.0001658016,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01291395,"about_ca_topic_score_gemma":0.002918391,"domain_scores_codex":[0.9978994,0.0003729629,0.0004045371,0.0005311694,0.0003849911,0.0004069966],"domain_scores_gemma":[0.9980718,0.0002340993,0.0002504669,0.001271611,0.00008387262,0.00008811137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008569016,0.000894688,0.1898397,0.00361822,0.000170011,0.0003088519,0.03480665,0.2595531,0.003835275,0.4515343,0.007562461,0.0477911],"study_design_scores_gemma":[0.0004544285,0.00007930207,0.02575871,0.004089683,0.000005259937,0.00001089855,0.0002939852,0.9563622,0.00166291,0.01036195,0.0001731931,0.0007475171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1436171,0.00006456923,0.8484333,0.001340592,0.002889024,0.0004366543,0.000004301496,0.001935959,0.001278483],"genre_scores_gemma":[0.9752646,0.000009947054,0.02349844,0.000113113,0.0001457242,0.00007800089,0.00008147334,0.00002969035,0.0007789775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8316475,"threshold_uncertainty_score":0.9999509,"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."}}