{"id":"W4210296881","doi":"10.2196/31536","title":"The Easy-to-Use SARS-CoV-2 Assembler for Genome Sequencing: Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministero della Salute","keywords":"Genome; Sequence assembly; Amplicon; Amplicon sequencing; Pipeline (software); Computational biology; DNA sequencing; Ion semiconductor sequencing; Computer science; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Whole genome sequencing; Table (database); Biology; Genetics; Data mining; Gene; Polymerase chain reaction; Infectious disease (medical specialty); Operating system; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0007177292,0.0002004999,0.0002975041,0.0003970919,0.0007938173,0.0001025623,0.0003736317,0.0001624607,0.000001766305],"category_scores_gemma":[0.0001452347,0.0001373426,0.00006121764,0.0005483417,0.0001175901,0.0000764957,0.000719725,0.0004458693,0.00002968495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002980017,"about_ca_system_score_gemma":0.0003909575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002859474,"about_ca_topic_score_gemma":0.00005349155,"domain_scores_codex":[0.9982921,0.00002504439,0.0005018982,0.0002444845,0.0003915161,0.0005449482],"domain_scores_gemma":[0.9991063,0.0001238876,0.0001130699,0.0005142659,0.0001073302,0.0000351679],"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":[0.001854474,0.001363407,0.01671337,0.000637715,0.0009516322,0.0001445981,0.01258478,0.000003275278,0.773578,0.005027766,0.005692045,0.1814489],"study_design_scores_gemma":[0.001489358,0.00193204,0.001789487,0.000009925113,0.00002378022,0.0001083899,0.005206031,0.001689926,0.08784474,0.00008484662,0.8996053,0.000216195],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993162,0.0000800963,0.001006847,0.002153468,0.0001005936,0.003062024,0.00001704272,0.0001558997,0.0002620737],"genre_scores_gemma":[0.9635038,0.00001139737,0.007043425,0.02725845,0.00004116444,0.001942646,0.00001287157,0.00003996415,0.0001463262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8939132,"threshold_uncertainty_score":0.6105481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07170149080541961,"score_gpt":0.3491686388871386,"score_spread":0.277467148081719,"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."}}