{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00497146,0.001609904,0.0006622911,0.001153149,0.0006150869,0.001034192,0.001330297,0.001106507,0.004314407],"category_scores_gemma":[0.00306416,0.0006888381,0.001211814,0.000717381,0.0003171367,0.001093436,0.001217578,0.001340982,0.007539952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004331636,"about_ca_system_score_gemma":0.002206069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002110612,"about_ca_topic_score_gemma":0.002202605,"domain_scores_codex":[0.9977831,0.0006071083,0.000143226,0.0003167411,0.0008996393,0.0002502436],"domain_scores_gemma":[0.9982324,0.0001931703,0.0001560264,0.0002440372,0.0007899032,0.0003844454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008183152,0.001076878,0.008355087,0.001386688,0.0001792061,0.0008389891,0.0005693559,0.005575361,0.7038377,0.002773272,0.02405315,0.2505359],"study_design_scores_gemma":[0.0002628799,0.003410997,0.01524798,0.0003436114,0.0002557528,0.003328088,0.0002031094,0.04379439,0.7392755,0.001141536,0.1925026,0.0002335014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1754266,0.008444401,0.7625219,0.00263594,0.0006696486,0.004447246,0.01035392,0.01852461,0.01697572],"genre_scores_gemma":[0.1105828,0.00520855,0.8357322,0.0007950975,0.0001120009,0.001579729,0.03139367,0.004017802,0.01057811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00497146,"threshold_uncertainty_score":0.02629191,"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."}}