{"id":"W2395937579","doi":"","title":"QA System Metis Based on Semantic Graph Matching at NTCIR 6.","year":2007,"lang":"en","type":"article","venue":"NTCIR","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Metis; Artificial intelligence; Natural language processing; Graph; Matching (statistics); Information retrieval; World Wide Web; Theoretical computer science; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004483553,0.0008587242,0.001336668,0.004336286,0.001711851,0.003229402,0.001525404,0.00149607,0.02697256],"category_scores_gemma":[0.009658419,0.0006076961,0.001175006,0.002124614,0.0004650592,0.005165939,0.001868506,0.001285917,0.01581861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648544,"about_ca_system_score_gemma":0.003667305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01657999,"about_ca_topic_score_gemma":0.01226141,"domain_scores_codex":[0.9969119,0.001267541,0.0002378666,0.0006497231,0.0006783804,0.0002545599],"domain_scores_gemma":[0.9959158,0.001021349,0.0001740735,0.000876023,0.001811406,0.0002014424],"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":[0.002438436,0.0008324897,0.009152831,0.001736537,0.0004978697,0.0005669512,0.00136672,0.01653667,0.04964442,0.05006074,0.326384,0.5407824],"study_design_scores_gemma":[0.0005621996,0.0007422489,0.009726512,0.0001948958,0.0005006185,0.0006418305,0.001087448,0.5764121,0.09977302,0.04132903,0.2687714,0.0002587417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05056351,0.001433245,0.6749339,0.002327702,0.000907688,0.002058919,0.02713897,0.17156,0.06907608],"genre_scores_gemma":[0.3899547,0.0004046786,0.5016164,0.0004635754,0.0002997095,0.0007946591,0.07597366,0.00569749,0.0247952],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02697256,"threshold_uncertainty_score":0.09023219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568791733628213,"score_gpt":0.2348742655769794,"score_spread":0.2191863482406973,"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."}}