{"id":"W3093757469","doi":"10.1101/2020.10.20.346783","title":"High-throughput detection of antibodies targeting the SARS-CoV-2 Spike in longitudinal convalescent plasma samples","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Héma-Québec; Université de Montréal; McGill University; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Institutes of Health Research; Servier; U.S. Military HIV Research Program; Fondation du CHUM","keywords":"Antibody; Convalescent plasma; Virology; Spike (software development); Immunology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Flow cytometry; Biology; Glycoprotein; Coronavirus; Coronavirus disease 2019 (COVID-19); Virus; Pandemic; Medicine; Disease; Internal medicine; Infectious disease (medical specialty); Genetics","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":[0.0005964365,0.0003420695,0.0003440708,0.000520694,0.0002400574,0.0004351582,0.0002691312,0.0004253535,0.0007600391],"category_scores_gemma":[0.0004774575,0.0001656319,0.0002083341,0.0004211891,0.000149925,0.0001794552,0.0002225485,0.0005126286,0.0003577574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002150831,"about_ca_system_score_gemma":0.0002115932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008491853,"about_ca_topic_score_gemma":0.0008959993,"domain_scores_codex":[0.9995952,0.00005966578,0.0000261765,0.0001220385,0.0001450959,0.00005178744],"domain_scores_gemma":[0.9996893,0.00007782929,0.00004520474,0.00002573017,0.0001289727,0.00003299078],"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":[0.0002603263,0.0001914367,0.01543355,0.00008090894,0.00002831209,0.00005362033,0.00009426631,0.0002976401,0.9775393,0.00004771825,0.0003147555,0.005658067],"study_design_scores_gemma":[0.0000577709,0.00201614,0.157403,0.00002719131,0.00009329698,0.0008851273,0.0002483053,0.01227097,0.8232834,0.0002282817,0.003446893,0.0000395123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768736,0.0006538723,0.01900997,0.00008633177,0.00003453315,0.0001887424,0.002077418,0.0002957527,0.0007796329],"genre_scores_gemma":[0.9739028,0.0004490851,0.01953652,0.0001879242,0.00003982593,0.0003144648,0.003911156,0.00002408289,0.001634029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008491853,"threshold_uncertainty_score":0.003154278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05434029654765956,"score_gpt":0.3018262350262049,"score_spread":0.2474859384785453,"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."}}