{"id":"W4284961504","doi":"10.7717/peerj.13721","title":"Optimizing human coronavirus OC43 growth and titration","year":2022,"lang":"en","type":"article","venue":"PeerJ","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Canadian Institutes of Health Research","keywords":"Virology; Titer; Coronavirus; Biology; Virus; Vero cell; Pandemic; Rhinovirus; Human metapneumovirus; Biosecurity; Coronavirus disease 2019 (COVID-19); Medicine; Respiratory system; Respiratory tract infections; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001859721,0.00005932975,0.0001053096,0.00007613758,0.0002267939,0.00002183528,0.00005765583,0.00002154234,0.0000873099],"category_scores_gemma":[0.00004057528,0.00005843091,0.00002940267,0.000112512,0.00003496384,0.00004643462,0.00008900365,0.000195984,0.00001709761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005745338,"about_ca_system_score_gemma":0.00005900715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002735383,"about_ca_topic_score_gemma":0.00001608841,"domain_scores_codex":[0.9992521,0.00003014051,0.00009244026,0.0001610067,0.0003155329,0.0001487205],"domain_scores_gemma":[0.9997743,0.00003089255,0.00001994248,0.0001190241,0.00003249922,0.00002330649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002133096,0.0001838287,0.09121203,0.0001239442,0.00004891486,0.0001887364,0.001633635,0.000002747675,0.8938144,0.004157859,0.001977719,0.00644283],"study_design_scores_gemma":[0.007256624,0.002390726,0.1125102,0.00005307743,0.0001688895,0.0004339971,0.001842629,0.003195204,0.3675915,0.002686869,0.5012528,0.000617458],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795708,0.0004441502,0.0000780259,0.000592789,0.00006021978,0.0001725355,0.000004643417,0.00005498687,0.01902183],"genre_scores_gemma":[0.9874805,0.000002010402,0.000220897,0.01193989,0.0000951433,0.00004051567,0.000009039185,0.00001268812,0.0001992627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5262229,"threshold_uncertainty_score":0.2382743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07496685179933643,"score_gpt":0.3759914421754042,"score_spread":0.3010245903760678,"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."}}