{"id":"W3159708225","doi":"10.20944/preprints202103.0055.v1","title":"Semi-Covariance Coefficient Analysis of Spike Proteins from SARS-CoV-2 and other Coronaviruses for Viral Evolution and Characteristics Associated with Fatality","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Animal Virus Infections Studies","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa; Carleton University","funders":"Canadian Institutes of Health Research; Jiangsu University","keywords":"Covariance; Coronavirus; Matthews correlation coefficient; Pearson product-moment correlation coefficient; Spike (software development); Spike Protein; Correlation coefficient; Statistics; Nonlinear system; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Statistical physics; Coronavirus disease 2019 (COVID-19); Mathematics; Biology; Computer science; Physics; Artificial intelligence; Medicine","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":[0.0003616293,0.0002669008,0.0006717707,0.00003245644,0.0002114326,0.00004348868,0.000165155,0.0002301086,0.00005554002],"category_scores_gemma":[0.0004059177,0.000134837,0.0001490833,0.0003153375,0.0002603302,0.00005888818,0.0007243243,0.0002262506,0.000002245126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001079078,"about_ca_system_score_gemma":0.00002554454,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006849402,"about_ca_topic_score_gemma":0.006830378,"domain_scores_codex":[0.9982573,0.0001340033,0.0003971443,0.0007880606,0.0002096097,0.0002138182],"domain_scores_gemma":[0.9987656,0.0002188181,0.0005259788,0.0001746595,0.0002764533,0.00003852564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009507046,0.0001887341,0.7957729,0.00002799133,0.0009804113,8.369336e-7,0.0002168306,0.00003574373,0.2023491,0.00007175622,0.00000139362,0.0002593434],"study_design_scores_gemma":[0.0001438084,0.00008499229,0.9761526,0.0001383434,0.0008370386,4.629751e-7,0.0001536834,0.0007224136,0.02119301,0.0001266034,0.0001919338,0.0002551134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954331,0.000168686,0.0008385883,0.0000537175,0.00006592571,0.0007417087,0.002571652,0.00005840582,0.00006820157],"genre_scores_gemma":[0.9991785,0.00005354466,0.0001449388,0.00007046729,0.00005325125,0.0001559133,0.0003151256,0.00000311964,0.0000251585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.181156,"threshold_uncertainty_score":0.9997641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.152380081594951,"score_gpt":0.3305865732104609,"score_spread":0.1782064916155099,"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."}}