{"id":"W4317603924","doi":"10.1101/2023.01.18.524564","title":"Nanovirseq: dsRNA sequencing for plant virus and viroid detection by Nanopore sequencing","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant and Fungal Interactions Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Sherbrooke; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Centre Sève; Université de Sherbrooke","keywords":"Nanopore sequencing; Biology; Viroid; Illumina dye sequencing; Plant virus; Human virome; Deep sequencing; RNA; DNA sequencing; Metagenomics; Virology; Computational biology; Virus; Genetics; Gene; Genome","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005913525,0.0004518694,0.0003622116,0.0002286814,0.00032701,0.000206284,0.0003362274,0.0007041211,0.000005560089],"category_scores_gemma":[0.0003238898,0.000488008,0.0001450406,0.0001863053,0.0001043939,0.00001867763,0.0004540388,0.000569778,0.00002221354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003109411,"about_ca_system_score_gemma":0.0004608555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002561879,"about_ca_topic_score_gemma":0.00007282622,"domain_scores_codex":[0.9975745,0.00009262167,0.0004151526,0.001065399,0.0002560629,0.0005962517],"domain_scores_gemma":[0.9985424,0.00006561879,0.0002455422,0.0006382401,0.0002872763,0.000220882],"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":[0.0001179368,0.00002084351,0.0002154301,0.0002385823,0.0001651475,0.00002224338,0.000005382361,0.00003536664,0.9972156,0.00001094811,0.001944294,0.000008216148],"study_design_scores_gemma":[0.0003331585,0.0001316724,0.0005590199,0.0001592414,0.00005421404,3.368855e-7,0.00001003909,0.001414344,0.9819062,0.000002058059,0.01487534,0.000554388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895936,0.001212458,0.004597044,0.00006776753,0.001208159,0.0007902392,0.002361489,0.0001621726,0.000007064033],"genre_scores_gemma":[0.9963312,0.001263663,0.0009655792,0.00008719769,0.000685345,0.0004059868,0.00002646758,0.0001452628,0.00008934466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01530943,"threshold_uncertainty_score":0.9997572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02795028719490492,"score_gpt":0.2530736735316257,"score_spread":0.2251233863367208,"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."}}