{"id":"W3124643392","doi":"","title":"Multiple Choice Question Answering using a Large Corpus of Information","year":2020,"lang":"en","type":"dissertation","venue":"University of Minnesota Digital Conservancy (University of Minnesota)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de la Santé et des Services sociaux; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Computer science; Question answering; Artificial intelligence; Context (archaeology); Sentence; Knowledge base; Natural language; Embedding; Natural language processing; Information retrieval; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.005564447,0.001316093,0.001700432,0.004474325,0.002111632,0.00358142,0.001385007,0.002398554,0.01039981],"category_scores_gemma":[0.03061775,0.0008133294,0.0009694374,0.005027274,0.0006469748,0.006019167,0.00275342,0.002285298,0.009115929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001509273,"about_ca_system_score_gemma":0.001800518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008079968,"about_ca_topic_score_gemma":0.0154735,"domain_scores_codex":[0.9929094,0.003767284,0.0004002213,0.001765357,0.0008920363,0.0002657964],"domain_scores_gemma":[0.9567662,0.03695623,0.0005831008,0.002273679,0.002768274,0.0006525732],"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":[0.001845681,0.001940486,0.02294444,0.003203665,0.0005473569,0.001766426,0.005914636,0.009747988,0.02052873,0.005547552,0.373203,0.55281],"study_design_scores_gemma":[0.001137286,0.001244404,0.107834,0.001130703,0.001061861,0.002053714,0.01245096,0.4713465,0.03264343,0.04605326,0.3224885,0.0005554305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5618542,0.01567847,0.1521404,0.009074159,0.001094238,0.001862644,0.2021503,0.01801237,0.03813326],"genre_scores_gemma":[0.47792,0.002333598,0.175154,0.001094199,0.0005860935,0.001722398,0.3276554,0.0008121691,0.01272214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01039981,"threshold_uncertainty_score":0.03479087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522717530504221,"score_gpt":0.2007749269194985,"score_spread":0.1855477516144562,"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."}}