{"id":"W4205893596","doi":"10.32384/jeahil17465","title":"Building a Systematic Online Living Evidence Summary of COVID-19 Research","year":2021,"lang":"en","type":"article","venue":"Journal of EAHIL","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Medical Research Council","keywords":"Coronavirus disease 2019 (COVID-19); Pace; Relevance (law); Workflow; Pandemic; 2019-20 coronavirus outbreak; Data science; Crowdsourcing; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Quality (philosophy); Computer science; World Wide Web; Political science; Medicine; Geography","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006746718,0.0001302612,0.0008546507,0.0006190522,0.0001023473,0.00004641274,0.0002915645,0.0001012025,0.0001621331],"category_scores_gemma":[0.111902,0.0001069541,0.0002817747,0.0009807596,0.0001160164,0.0002100784,0.0002019791,0.0007793204,0.000006150066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007406833,"about_ca_system_score_gemma":0.003735889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000917681,"about_ca_topic_score_gemma":0.00003308613,"domain_scores_codex":[0.9959564,0.0008514946,0.001166424,0.0002081295,0.0015095,0.0003080649],"domain_scores_gemma":[0.9814983,0.01483495,0.0007001674,0.0005090524,0.001991021,0.0004665317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0008769435,0.004753075,0.06313056,0.3350534,0.001555023,0.01543625,0.009318946,0.002329557,0.3818634,0.0007960779,0.1832936,0.001593301],"study_design_scores_gemma":[0.002623942,0.002057485,0.0158363,0.9266333,0.001564011,0.008295288,0.005961592,0.002177591,0.01453389,0.0009001017,0.0189386,0.0004778904],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8078883,0.06272762,0.002530063,0.1255489,0.0006368649,0.0005645905,0.000008402771,0.00003407352,0.00006120119],"genre_scores_gemma":[0.975991,0.003126968,0.01098903,0.008497965,0.0007330498,0.000005695858,8.617694e-7,0.00004042897,0.0006150358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.59158,"threshold_uncertainty_score":0.8955788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2321527880595403,"score_gpt":0.4891142412848818,"score_spread":0.2569614532253415,"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."}}