{"id":"W4312515853","doi":"10.1109/iisa56318.2022.9904390","title":"Question Answering Using Semantic Query Graphs: A Replication Study","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Question answering; Artificial intelligence; RDF; Information retrieval; Natural language processing; Domain (mathematical analysis); Graph; Deep learning; Knowledge graph; Semantic Web; Theoretical computer science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02109111,0.001691791,0.001519022,0.003391001,0.001171402,0.002185349,0.002984162,0.002370588,0.005432954],"category_scores_gemma":[0.08718324,0.0005225166,0.002706842,0.002092436,0.001907271,0.008673845,0.003050174,0.002883948,0.003063393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002194157,"about_ca_system_score_gemma":0.002254728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01810057,"about_ca_topic_score_gemma":0.006981225,"domain_scores_codex":[0.9786448,0.01410171,0.001067941,0.003000051,0.002748971,0.0004364665],"domain_scores_gemma":[0.8971064,0.05649217,0.00178269,0.03135411,0.01216588,0.001098792],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005331437,0.01014158,0.07697681,0.005235475,0.002167786,0.0008508415,0.00680061,0.03809,0.0268715,0.01986995,0.07979756,0.7278665],"study_design_scores_gemma":[0.003365919,0.008457299,0.09979127,0.001679785,0.003097952,0.002784397,0.007488156,0.5078501,0.05337635,0.06517597,0.2462053,0.0007275325],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.752898,0.01233898,0.1572987,0.008933481,0.001503556,0.004909851,0.01134293,0.01260851,0.03816584],"genre_scores_gemma":[0.8757493,0.001638091,0.09576812,0.002366861,0.0005309497,0.00139053,0.01530336,0.00102816,0.006224453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9789089,"threshold_uncertainty_score":0.1115417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04326958801382588,"score_gpt":0.301854366465917,"score_spread":0.2585847784520911,"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."}}