{"id":"W4255970554","doi":"10.21203/rs.3.rs-88429/v3","title":"A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Genome; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pathogenicity; Biology; Computational biology; Adaptability; Coronavirus disease 2019 (COVID-19); Strain (injury); Lineage (genetic); Genetics; Evolutionary biology; Computer science; Disease; Gene; Infectious disease (medical specialty); 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":[0.001522774,0.0008221462,0.001001909,0.001909054,0.0007972623,0.002210981,0.001986403,0.001323721,0.002776511],"category_scores_gemma":[0.003346617,0.0004484207,0.001782503,0.001281076,0.0006948698,0.00105171,0.001936955,0.001328795,0.0004763268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001008011,"about_ca_system_score_gemma":0.001973374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01891332,"about_ca_topic_score_gemma":0.02377193,"domain_scores_codex":[0.99935,0.0002083493,0.00005317061,0.0001904883,0.0001233849,0.0000745527],"domain_scores_gemma":[0.998848,0.0006572762,0.00008963114,0.00008294111,0.0002205955,0.0001014953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001817515,0.0003266197,0.008037205,0.0001851706,0.0003249478,0.000419549,0.0002475629,0.8755569,0.002012731,0.02651044,0.002916881,0.08328025],"study_design_scores_gemma":[0.000003119369,0.000008907807,0.0001372602,0.000005831445,0.000009585689,0.000007296777,0.00001322457,0.9953119,0.00007985596,0.004150935,0.0002694416,0.000002716663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03873111,0.0005085016,0.9541051,0.001149377,0.00007876762,0.0001430558,0.0007368727,0.00164352,0.002903626],"genre_scores_gemma":[0.4178739,0.0003731545,0.5769123,0.0004606918,0.0001234118,0.0003426266,0.001665772,0.0001232695,0.002124876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01891332,"threshold_uncertainty_score":0.03760648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0431936475099335,"score_gpt":0.3807619749537697,"score_spread":0.3375683274438362,"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."}}