{"id":"W3197846610","doi":"10.1051/e3sconf/202129203069","title":"Lessons learned from COVID-19 outbreaks profile in combination with pathology, diagnosis, treatment and vaccines","year":2021,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Immune responses and vaccinations","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Pandemic; Outbreak; Public health; China; Government (linguistics); Coronavirus disease 2019 (COVID-19); Disease; Medicine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Infectious disease (medical specialty); Intensive care medicine; Economic growth; Environmental health; Political science; Virology; Pathology; Economics","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":[0.0105529,0.0009884071,0.0009911248,0.00206829,0.0007807852,0.00323321,0.001051612,0.00196617,0.002337834],"category_scores_gemma":[0.01574872,0.0003385697,0.0008942965,0.001142502,0.001668542,0.005901687,0.001539406,0.004150101,0.000660557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003309464,"about_ca_system_score_gemma":0.006948031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00746555,"about_ca_topic_score_gemma":0.0182748,"domain_scores_codex":[0.9961564,0.001995077,0.0004525358,0.0003431131,0.000682865,0.000370064],"domain_scores_gemma":[0.9901546,0.003443797,0.0007369067,0.0005775362,0.003909371,0.001177757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003189542,0.0003047519,0.0484204,0.004943992,0.0003081226,0.002547876,0.005282164,0.001692275,0.003454749,0.01261752,0.1544781,0.7656311],"study_design_scores_gemma":[0.00008213891,0.0009637072,0.06830974,0.01311264,0.0008481643,0.004742664,0.02751078,0.003893575,0.006556778,0.06723131,0.8064684,0.0002800469],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05851717,0.3771995,0.014939,0.509106,0.01855034,0.0001698734,0.0007688297,0.0004111991,0.02033815],"genre_scores_gemma":[0.479142,0.3623779,0.03438915,0.08731642,0.02487785,0.0001447652,0.0009285481,0.0002211569,0.0106022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0105529,"threshold_uncertainty_score":0.05580974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05062386616143186,"score_gpt":0.3153005042715585,"score_spread":0.2646766381101266,"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."}}