{"id":"W3025078982","doi":"10.1101/2020.05.12.088716","title":"Potent neutralizing antibodies from COVID-19 patients define multiple targets of vulnerability","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Infection and Immunity; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Universiteit van Amsterdam; Amsterdam University Medical Centers; Bill and Melinda Gates Foundation","keywords":"Epitope; Antibody; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Spike Protein; Neutralizing antibody; Vulnerability (computing); 2019-20 coronavirus outbreak; Antigen; Immunology; Biology; Medicine; Outbreak; Disease; Computer science; Infectious disease (medical specialty); Computer security; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007088429,0.0007852952,0.001514798,0.0003793145,0.0002015231,0.0001006007,0.0007038842,0.0006839548,0.00003043316],"category_scores_gemma":[0.003971903,0.0007797983,0.0004661525,0.0005414443,0.0003600144,0.0001287708,0.001134894,0.001465822,0.0000676167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007421742,"about_ca_system_score_gemma":0.002085694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001974902,"about_ca_topic_score_gemma":0.00002151843,"domain_scores_codex":[0.9948925,0.0003237725,0.001140153,0.001698751,0.001134927,0.0008098741],"domain_scores_gemma":[0.9956728,0.000404155,0.0006078326,0.001857708,0.0009668921,0.0004905845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004147562,0.0005350422,0.548777,0.00150089,0.000297148,0.0001042547,0.00004532059,0.00000928598,0.4479136,0.00003125795,0.0003677915,0.000003666313],"study_design_scores_gemma":[0.00252513,0.0001824599,0.4917383,0.0003607187,0.0001865459,1.08003e-8,0.000008070528,0.0004714568,0.4936365,0.0000138946,0.01030438,0.0005725185],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917454,0.002035005,0.001031299,0.0009034595,0.0007486221,0.001734731,0.001354549,0.0004302989,0.00001666873],"genre_scores_gemma":[0.9833415,0.00007675402,0.003519113,0.0123246,0.000412192,0.0001643027,0.000004920504,0.0001561567,4.578108e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05703872,"threshold_uncertainty_score":0.9994653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04948078299175647,"score_gpt":0.3053115363156871,"score_spread":0.2558307533239307,"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."}}