{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00135829,0.0009244815,0.0007214315,0.0006506379,0.0005577743,0.0008565374,0.0007196959,0.0009355156,0.002200199],"category_scores_gemma":[0.001914399,0.0003627569,0.0005709605,0.0006040473,0.0002725345,0.0004350189,0.0008460447,0.0009343125,0.001574468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003420771,"about_ca_system_score_gemma":0.0004965983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002425612,"about_ca_topic_score_gemma":0.002449579,"domain_scores_codex":[0.9986345,0.0002969142,0.0001722751,0.0002632861,0.0004480881,0.0001849251],"domain_scores_gemma":[0.9994586,0.0001366233,0.00005853343,0.00007371456,0.000221298,0.00005128761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002416302,0.0003904667,0.001524131,0.0002511737,0.00001932051,0.0001741695,0.0004110058,0.001678851,0.9855617,0.0003354634,0.0009340711,0.00847814],"study_design_scores_gemma":[0.00002746228,0.001394731,0.003526795,0.0001621394,0.00005648161,0.0002225621,0.000219805,0.004472781,0.9682907,0.0002277075,0.02134286,0.00005594867],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9266579,0.00626941,0.03915972,0.0005954985,0.0005749117,0.002917309,0.005058574,0.0009713407,0.01779533],"genre_scores_gemma":[0.836543,0.009976594,0.1207349,0.0006844813,0.0001453874,0.003986721,0.01280573,0.0007198638,0.01440342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002425612,"threshold_uncertainty_score":0.007360339,"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."}}