{"id":"W3094469519","doi":"10.1017/dmp.2020.406","title":"Emerging Standards and the Hybrid Model for Organizing Scientific Events During and After the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"Disaster Medicine and Public Health Preparedness","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus Infections; Betacoronavirus; Virology; Computer science; Medicine; Outbreak; Infectious disease (medical specialty)","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":[],"consensus_categories":[],"category_scores_codex":[0.00273582,0.0002079475,0.0003274267,0.0001047372,0.001194678,0.0004210267,0.0002695307,0.00003607745,0.00006292496],"category_scores_gemma":[0.001715018,0.0001126366,0.00002666123,0.0003888783,0.000668312,0.0009048634,0.0004424504,0.0001540885,0.000001358513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004297068,"about_ca_system_score_gemma":0.0002478908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002421794,"about_ca_topic_score_gemma":0.000243958,"domain_scores_codex":[0.9981551,0.00005302753,0.0004174163,0.0005184371,0.0004077979,0.0004482242],"domain_scores_gemma":[0.9989779,0.0001665984,0.0002131567,0.00027009,0.0002234323,0.0001488364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004124918,0.0001522763,0.1919677,0.02412929,0.0002913778,0.00001871651,0.5820886,0.0001708399,0.0001612787,0.01147197,0.09419788,0.09122513],"study_design_scores_gemma":[0.009072791,0.00006319014,0.003269784,0.0007584774,0.0002568843,0.000144402,0.104857,0.2917476,0.000001669167,0.007268244,0.5816336,0.0009263683],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8681318,0.002161522,0.02182729,0.1064609,0.0003811765,0.0008426071,0.00006084339,0.00007344436,0.00006039707],"genre_scores_gemma":[0.9639183,0.0002611167,0.0000194508,0.03494235,0.0006264338,0.0001019815,0.00004985583,0.00002228888,0.00005819785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4874357,"threshold_uncertainty_score":0.9188619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1609561925781463,"score_gpt":0.3698223061486224,"score_spread":0.2088661135704762,"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."}}