{"id":"W4223916144","doi":"10.7717/peerj.13300","title":"PipeCoV: a pipeline for SARS-CoV-2 genome assembly, annotation and variant identification","year":2022,"lang":"en","type":"article","venue":"PeerJ","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Vale Canada Limited","keywords":"Genome; Identification (biology); Pipeline (software); Computational biology; Annotation; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Biology; Coronavirus; Computer science; Data science; Virus; Genetics; Gene; Infectious disease (medical specialty); 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":[],"consensus_categories":[],"category_scores_codex":[0.0009290609,0.0001208306,0.0002181198,0.0002267441,0.0002596739,0.00006285522,0.0001255083,0.0000474028,0.00001429557],"category_scores_gemma":[0.0003118539,0.0001224362,0.00007727762,0.0003063293,0.00003650754,0.000104795,0.0001003471,0.000189242,0.00003458333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001556717,"about_ca_system_score_gemma":0.0001669661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000133783,"about_ca_topic_score_gemma":0.00002599663,"domain_scores_codex":[0.9985183,0.00006309293,0.0002998374,0.0003837793,0.000457955,0.0002770548],"domain_scores_gemma":[0.999234,0.0001278594,0.00009162198,0.0003047618,0.0002158568,0.0000258839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003849762,0.000180342,0.001059109,0.0001399871,0.00003463284,0.00003150939,0.0005588598,0.000002341204,0.985612,0.0005909322,0.003513724,0.007891583],"study_design_scores_gemma":[0.004136109,0.0005734198,0.02450285,0.00001699482,0.0001685344,0.0002399622,0.0005040691,0.03618004,0.2679763,0.00179174,0.6635872,0.0003228592],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718115,0.0005569864,0.02184119,0.003389969,0.000232397,0.001150359,0.00009993836,0.00009858271,0.0008190991],"genre_scores_gemma":[0.9835737,0.000005101153,0.000477719,0.01455114,0.0002522027,0.000518529,0.0001282846,0.00003519016,0.0004581089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7176358,"threshold_uncertainty_score":0.4992804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06067697167216344,"score_gpt":0.3689058206208236,"score_spread":0.3082288489486602,"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."}}