{"id":"W3189074187","doi":"10.1101/2021.07.29.454261","title":"COVID-ONE-humoral immune: The One-stop Database for COVID-19-specific Antibody Responses and Clinical Parameters","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Immune system; Coronavirus disease 2019 (COVID-19); Humoral immunity; Immunology; Antibody; Immunity; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Biology; Disease; Medicine; Virology; Infectious disease (medical specialty); Internal medicine","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":[0.002126179,0.002315318,0.002345234,0.005084925,0.000473131,0.002480831,0.002299493,0.002148194,0.01773709],"category_scores_gemma":[0.008082192,0.0007803697,0.001608893,0.004379697,0.000368988,0.001294114,0.002566967,0.001249288,0.02182646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000839648,"about_ca_system_score_gemma":0.00209491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003069592,"about_ca_topic_score_gemma":0.004048133,"domain_scores_codex":[0.9979533,0.0002443205,0.0005084377,0.0006550727,0.0004513762,0.0001874885],"domain_scores_gemma":[0.9950512,0.001213425,0.001176272,0.001087322,0.000871032,0.0006007428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003757701,0.0004843872,0.07272074,0.01201629,0.0009917141,0.0009216684,0.0002944148,0.003007045,0.01712931,0.002839751,0.8223482,0.06348881],"study_design_scores_gemma":[0.001052418,0.0005701273,0.09626818,0.001724164,0.0005697613,0.001774468,0.0002179773,0.006915249,0.0118963,0.003586402,0.8751505,0.0002745571],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004615154,0.001055356,0.001951795,0.0001106894,0.00008242978,0.0001358106,0.9857928,0.004898476,0.001357385],"genre_scores_gemma":[0.005385675,0.0002835183,0.002307544,0.0001413492,0.00002278582,0.0001739969,0.9910935,0.000291597,0.0003001217],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01773709,"threshold_uncertainty_score":0.05933648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1224488477874352,"score_gpt":0.3924753530364403,"score_spread":0.270026505249005,"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."}}