{"id":"W4297941332","doi":"10.1109/compsac54236.2022.00210","title":"A Question-Answering System on COVID-19 Scientific Literature","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Public Health; Public Health Ontario; University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Computer science; Pipeline (software); Gold standard (test); Question answering; Information retrieval; Data science; Natural language processing; Infectious disease (medical specialty); Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.004641403,0.002501455,0.001413718,0.01259484,0.001468991,0.002478573,0.002831983,0.003215157,0.02149648],"category_scores_gemma":[0.01809754,0.0006287172,0.001600458,0.004506289,0.0005891987,0.006934072,0.005476877,0.00192591,0.01472913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002556013,"about_ca_system_score_gemma":0.003322508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008714993,"about_ca_topic_score_gemma":0.01385814,"domain_scores_codex":[0.9968365,0.0007548338,0.0003950426,0.001244735,0.0006055371,0.0001633291],"domain_scores_gemma":[0.9923625,0.003966049,0.0004232789,0.0008076705,0.001989295,0.0004511374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008178961,0.0007577425,0.007935599,0.005743162,0.0002416148,0.001246029,0.001684308,0.005612521,0.02172385,0.009232783,0.5675777,0.3774268],"study_design_scores_gemma":[0.0005874299,0.000826636,0.01216603,0.001167456,0.0003759332,0.001518003,0.002739665,0.3328622,0.03823655,0.03624716,0.5729831,0.0002899244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.07692255,0.008625188,0.2195167,0.008412114,0.002039954,0.004567158,0.3562673,0.2902755,0.03337346],"genre_scores_gemma":[0.09567554,0.001190476,0.3774567,0.00286895,0.00047559,0.001878331,0.5101914,0.001677892,0.008585172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02149648,"threshold_uncertainty_score":0.07191288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206318910086901,"score_gpt":0.2621014927189638,"score_spread":0.2400383036180948,"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."}}