{"id":"W2296338095","doi":"10.1109/ichi.2015.98","title":"Golden Retriever: Question Retrieval System","year":2015,"lang":"en","type":"article","venue":"","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Information retrieval; Semantic similarity; Relevance (law); Search engine indexing; Similarity (geometry); Semantics (computer science); Analytics; Semantic computing; Probabilistic latent semantic analysis; Data science; Artificial intelligence; Semantic 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":[0.001068557,0.0001109038,0.0001595315,0.00008462177,0.00006879155,0.0002037199,0.0006176952,0.0001009744,0.000002494693],"category_scores_gemma":[0.0001085872,0.00008928627,0.00004352575,0.000467524,0.0000160392,0.0004248749,0.000140223,0.00009076923,0.0006175669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002273848,"about_ca_system_score_gemma":0.0001000592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000234251,"about_ca_topic_score_gemma":0.00000431542,"domain_scores_codex":[0.9986117,0.0001337661,0.0002367432,0.0003224877,0.0004622669,0.0002329787],"domain_scores_gemma":[0.9989416,0.00004010355,0.00007013587,0.0005772603,0.0001606102,0.0002103055],"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":[0.00003042215,0.00004391062,0.00355344,0.00004904209,0.00001832519,0.0000748226,0.002271171,0.00002878289,0.0008993893,0.8909235,0.09909984,0.003007342],"study_design_scores_gemma":[0.005284211,0.001760646,0.004068716,0.001071442,0.00003395485,0.001927067,0.004106107,0.4648114,0.04768291,0.004197809,0.4623456,0.002710151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03074868,0.0008883593,0.788146,0.001484949,0.005935711,0.0003536356,0.000001944049,0.0028002,0.1696406],"genre_scores_gemma":[0.9807217,0.000001957203,0.009097639,0.00008982545,0.0002810244,0.000003074016,0.000001537887,0.000008518013,0.009794715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.949973,"threshold_uncertainty_score":0.793778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03166001956969938,"score_gpt":0.2619031551959478,"score_spread":0.2302431356262484,"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."}}