{"id":"W3019119825","doi":"10.1371/journal.pone.0232391","title":"Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1034,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genome; Computational biology; Genomics; Biology; Novel virus; Virus classification; Decision tree; Virus; Artificial intelligence; Computer science; Genetics; Gene","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":[0.0002859693,0.0001474307,0.0002312885,0.00005011852,0.0001315009,0.00001792196,0.000150153,0.0001129239,0.00001745716],"category_scores_gemma":[0.001331092,0.0001489817,0.00006521791,0.0001036883,0.00004064423,0.000004598138,0.0001188465,0.0001910865,0.000001358945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002695897,"about_ca_system_score_gemma":0.0001029887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004330067,"about_ca_topic_score_gemma":0.00001423504,"domain_scores_codex":[0.9990098,0.00008072358,0.0003457865,0.0002525281,0.0001536251,0.0001575462],"domain_scores_gemma":[0.9991982,0.00005419012,0.0002853336,0.0002229712,0.0001141682,0.0001251379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001731275,0.0008776844,0.009383263,0.000359052,0.0002419524,0.00001086585,0.001110002,0.004992246,0.9822176,0.00001785919,0.00002092843,0.0005953898],"study_design_scores_gemma":[0.006157304,0.005101823,0.00154444,0.0000447969,0.0007294671,0.0001326621,0.004115591,0.8879853,0.09081358,0.000008093859,0.002653737,0.0007131335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9753528,0.0003748089,0.02315808,0.000191541,0.00001682152,0.000766188,0.00007456903,0.00002793665,0.00003726985],"genre_scores_gemma":[0.9755908,0.00003051386,0.02348925,0.0004653906,0.0001508555,0.00002457219,0.0001993678,0.00003090173,0.00001836813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.891404,"threshold_uncertainty_score":0.6075297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024710124406709,"score_gpt":0.3021214621668543,"score_spread":0.1996504497261834,"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."}}